M.Phil Computer Science Image Processing Projects

M.Phil Computer Science Image Processing Projects
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List Link : http://kasanpro.com/projects-list/m-phil-computer-science-image-processing-projects
Title :Texture Analysis and Classification with Linear Regression Model Based on Wavelet Transform
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/texture-analysis-classification-based-wavelet-transform
Abstract : The wevelet transform as an important multiresolution analysis tool has commonly applied to texture
analysis and classification. Nevertheless, it ignores the structural information while capturing the spectral information
of the texture image at different scales. In this paper, we propose a texture analysis and classification approach with
the linear regression model based on the wavelet transform. This method is motivated by the observation that there
exists a distinctive correlation between the sample images, belonging to the same kind of texture, at different
frequency regions obtained by 2-D wavelet packet transform. Experimentally, it was observed that this correlation
verious from texture to texture. The linear regression model is empolyed to analyze this correlation and extract texture
feature that characterize the samples. Therefore, our method considers not only the frequency regions but also the
correlation betweem these regions. In contrast, the pyramid-structured wavelet transform (PSWT) and the tree-
structured wavelet transform (TSWT) do not consider the correlation between different frequency regiond.
Experiments show that our method significantly improves the texture classification rate in comparison with the
multiresolution methods, including PSWT, TSWT, the Gabor transform, and some recently proposed methods derved
from these.
Title :Image Inpainting by Patch Propagation using Patch Sparsity
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/image-inpainting-patch-propagation-patch-sparsity
Abstract : This paper introduces a novel examplar-based in-painting algorithm through investigating the sparsity of
natural image patches. Two novel concepts of sparsity at the patch level are proposed for modeling the patch priority
and patch representation, which are two crucial steps for patch propagation in the examplar-based inpainting
approach. First, patch structure sparsity is designed to measure the confidence of a patch located at the image
structure (e.g., the edge or corner) by the sparseness of its nonzero similarities to the neighboring patches. The patch
with larger structure sparsity will be assigned higher priority for further inpainting. Second, it is assumed that the patch
to be filled can be represented by the sparse linear combination of candidate patches under the local patch
consistency constraint in a framework of sparse representation. Compared with the traditional examplar-based
inpainting approach, structure sparsity enables better discrimination of structure and texture, and the patch sparse
representation forces the newly inpainted regions to be sharp and consistent with the surrounding textures.
Experiments on synthetic and natural images show the advantages of the proposed approach.
Title :Medical Image Fusion via an Effective Wavelet-Based Approach
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/medical-image-fusion-effective-wavelet-based
Abstract : A novel wavelet-based approach for medical image fusion is presented, which is developed by taking into
not only account the characteristics of human visual system (HVS) but also the physical meaning of the wavelet
coefficients. After the medical images to be fused are decomposed by the wavelet transform, different-fusion schemes
for combining the coefficients are proposed: coefficients in low-frequency band are selected with a visibility-based
scheme, and coefficients in high-frequency bands are selected with a variance based method. To overcome the
presence of noise and guarantee the homogeneity of the fused image, all the coefficients are subsequently performed
by a window-based consistency verification process. The fused image is finally constructed by the inverse wavelet
transform with all composite coefficients. To quantitatively evaluate and prove the performance of the proposed
method, series of experiments and comparisons with some existing fusion methods are carried out in the paper.
Experimental results on simulated and real medical images indicate that the proposed method is effective and can get
satisfactory fusion results.

Title :Face Recognition by Exploring Information Jointly in Space, Scale and Orientation
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/face-recognition-exploring-information-jointly-space-scale-orientation
Abstract : Information jointly contained in image space, scale and orientation domains can provide rich important
clues not seen in either individual of these domains. The position, spatial frequency and orientation selectivity
properties are believed to have an important role in visual perception. This paper proposes a novel face
representation and recognition approach by exploring information jointly in image space, scale and orientation
domains. Specifically, the face image is first decomposed into different scale and orientation responses by convolving
multiscale and multior- ientation Gabor filters. Second, local binary pattern analysis is used to describe the
neighboring relationship not only in image space, but also in different scale and orientation responses. This way,
information from different domains is explored to give a good face representation for recognition. Discriminant
classification is then performed based upon weighted histogram intersection or conditional mutual information with
linear discriminant analysis techniques. Extensive experimental results on FERET, AR, and FRGC ver 2.0 databases
show the significant advantages of the proposed method over the existing ones.
Title :Global Ridge Orientation Modeling for Partial Fingerprint Identification
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/global-ridge-orientation-modeling-partial-fingerprint-identification
Abstract : Identifying incomplete or partial fingerprints from a large fingerprint database remains a difficult challenge
today. Existing studies on partial fingerprints focus on one-to-one matching using local ridge details. In this paper, we
investigate the problem of retrieving candidate lists for matching partial fingerprints by exploiting global topological
features. Specifically, we propose an analytical approach for reconstructing the global topology representation from a
partial fingerprint. Firstly, we present an inverse orientation model for describing the reconstruction problem. Then, we
provide a general expression for all valid solutions to the inverse model. This allows us to preserve data fidelity in the
existing segments while exploring missing structures in the unknown parts. We have further developed algorithms for
estimating the missing orientation structures based on some a priori knowledge of ridge topology features. Our
statistical experiments show that our proposed model-based approach can effectively reduce the number of
candidates for pair-wised fingerprint matching, and thus significantly improve the system retrieval performance for
partial fingerprint identification.
Title :Energy-Efficient Localized Routing in Random Multihop Wireless Networks
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/energy-efficient-localized-routing-random-multihop-wireless-networks
Abstract : A number of energy-aware routing protocols were proposed to seek the energy efficiency of routes in
multihop wireless networks. Among them, several geographical localized routing protocols were proposed to help
making smarter routing decision using only local information and reduce the routing overhead. However, all proposed
localized routing methods cannot guarantee the energy efficiency of their routes. In this paper, we first give a simple
localized routing algorithm, called Localized Energy-Aware Restricted Neighborhood routing (LEARN), which can
guarantee the energy efficiency of its route if it can find the route successfully. We then theoretically study its critical
transmission radius in random networks which can guarantee that LEARN routing finds a route for any source and
destination pairs asymptotically almost surely. We also extend the proposed routing into three-dimensional (3D)
networks and derive its critical transmission radius in 3D random networks. Simulation results confirm our theoretical
analysis of LEARN routing and demonstrate its energy efficiency in large scale random networks.
Title :Extraction of Head and Face Boundaries for Face Detection Application
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/extraction-head-face-boundaries-face-detection-application
Abstract : Face detection is an importent first step to many advanced computer vision, biometrics and multimedia
applications such as face tracking, face recognition and video surveillance. In this paper, a faster face detection
system is proposed and the method of extracking head and face boundaries along with its facial features has been
utilized. Initially, boundary tracking is employed to extract the head and face boundaries from the image. This
boundary tracking is done with the help of BW (Black and White) tracking function. Facial features are extracted using

gabor filter algorithm. The neural network employed for face detection is based on multi layer neurons architecture
while is a feed forword network. This approch is even applicable for detecting faces in cluster images. Experimental
results show that the proposed approach can perfrom the extraction human head, face boundaries and detection of
face succesfully. The proposed technique can be applied for images with single face as well as nultiple faces and the
faces are detected succesfully with high detection rate when compared to the adaboost technique of face detection.
Title :Color Image Quantization Techique based on Image Compression for Power Consumption for Embedded
Sytems
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/color-image-quantization-techique-based-image-compression
Abstract : Data transmission over the Internet is prevalent and the development of efficient algorithms for
compressing such data in order to achieve reduced bandwidth has been an active research. With increased demand
for exchanges of datas over the Internet, research for data compression is more intense than ever before. Computing
techniques that would considerably reduce the number of colours in an image that occupies less space and
bandwidth for transmission over networks form an active research. The less space and less bandwidth will also
reduce the memory access for displaying image and this will lead to saving considerable amount of power in a
resource constrained battery operated embedded system. In this project a new colour quantisation (CQ) technique is
introduced. The CQ technique is based on image split into sub-images and the use of self-organised neural network
classifiers (SONNC). Initially, the dominant colours of each sub-image are extracted through SONNCs and then are
used for the quantisation of the colours of the entire image. In addition, for the estimation of the proper number of
dominant image colours, a new algorithm based on the projection of the image colours into the first two principal
components is proposed. Applying a systematic design methodology to the developed CQ algorithm, an efficient
embedded architecture based on the ARM7 processor achieving high-speed processing and less energy
consumption, is derived.
http://kasanpro.com/ieee/final-year-project-center-tiruppur-reviews
Title :Data Hiding in Motion Vectors of Compressed Video Based on Their Associated Prediction Error
Language : C#
Project Link : http://kasanpro.com/p/c-sharp/video-data-hiding-based-prediction-error
Abstract : This paper deals with data hiding in compressed video. Unlike data hiding in images and raw video which
operates on the images themselves in the spatial or transformed domain which are vulnerable to steganalysis, we
target the motion vectors used to encode and reconstruct both the forward predictive (P)-frame and bidirectional
(B)-frames in compressed video. The choice of candidate subset of these motion vectors are based on their
associated macro block prediction error, which is different from the approaches based on the motion vector attributes
such as the magnitude and phase angle, etc. A greedy adaptive threshold is searched for every frame to achieve
robustness while maintaining a low prediction error level. The secret message bit stream is embedded in the least
significant bit of both components of the candidate motion vectors. The method is implemented and tested for hiding
data in natural sequences of multiple groups of pictures and the results are evaluated. The evaluation is based on two
criteria: minimum distortion to the reconstructed video and minimum overhead on the compressed video size. Based
on the aforementioned criteria, the proposed method is found to perform well and is compared to a motion vector
attribute-based method from the literature.
Title :A Medical Image Archive Solution in the Cloud
Language : C#
Project Link : http://kasanpro.com/p/c-sharp/medical-image-archive-solution-cloud
Abstract : Growing long-term cost of managing an onsite medical imaging archive has been a subject which the
health care industry struggles with. Based on the current trend, it is estimated that over 1 billion diagnostic imaging
procedures will be performed in the United States during year 2014, generating about 100 Peta bytes of data. The
high volume of medical images is leading to scalability and maintenance issues with healthcare providers' onsite
picture archiving and communication system and network. Cloud computing promises lower cost, high scalability,
availability and disaster recoverability which can be a natural solution some of the problems we faced for long-term
medical image archive. A prototype system was implemented to study such as solution on one of the industry leading
cloud computing platform, Microsoft Windows Azure. It includes a Digital Imaging and Communications in Medicine
(DICOM) server which handles standard store/query/retrieve requests; a DICOM image indexer that parses the
metadata and stores them in a SQL Azure database; and a web UI for searching and viewing archived images based
on patient and image attributes. The comprehensive tools and functionality of Windows Azure made it an ideal

platform to develop and deploy this kind of service oriented applications.
Title :A Double Thresholding Method for Cancer Stem Cell Detection
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/double-thresholding-method-cancer-stem-cell-detection
Abstract : Image analysis of cancer cells is important for cancer diagnosis and therapy, because it recognized as the
most efficient and effective way to observe its proliferation. For the purpose of adaptive and accurate cancer cell
image segmentation, a double threshold segmentation method is proposed in this paper. Based on a single
gray-value histogram of the RGB color space, a double threshold, the key parameters of threshold segmentation
component can be fixed histogram. As by a fitted-curve reasonable of thresholds the RGB confirmed, binary
segmentation dependent on two thresholds, will be put into practice and result in binary image. With the
post-processing of mathematical morphology and division of whole image, the better segmentation result can be
finally achieved. By the comparison with other advanced segmentation methods such as level set and active contour,
the proposed double thresholding has been found as the simplest strategy with shortest processing time as well as
highest accuracy. The proposed method can be effectively used in the detection and recognition of cancer stem cells
in images.
Title :The Automatic Detection Algorithm of Tongue Cancer Stem Cells Based on Fuzzy Pattern Recognition
Language : Matlab
Project Link :
http://kasanpro.com/p/matlab/detection-algorithm-tongue-cancer-stem-cells-based-fuzzy-pattern-recognition
Abstract : In this paper, we present a novel recognition algorithm for detecting tongue cancer stem cells with respect
to appropriate scaling factors. Our method can be achieved by computer image processing in the condition that the
cancer cells are undifferentiated or slightly differentiated, which is of important research significance in the realm of
oral medicine. According to the biological natures of tongue cancer stem cells, we select the curvature variance of cell
contour, the nuclear- cytoplasmic area ratio, and the average optical density of cytoplasm as the measurement
parameters. Using these three biological parameters, the characteristics of cancerous tumor cells can be described
and thus classified. Therefore, those cells can be categorized under the principle of maximum degree of membership
in fuzzy pattern recognition algorithms. In this way, the tongue cancer stem cells can be automatically detected.
Desirable recognition results given by our experiments have substantiated the efficiency of our algorithm.
Title :Motion human detection based on background subtraction
Language : C#
Project Link : http://kasanpro.com/p/c-sharp/motion-human-detection-based-background-subtraction
Abstract : According to the result of moving object detection research on video sequences, this paper proposes a
new method to detect moving object based on background subtraction. First of all, we establish a reliable background
updating model based on statistical and use a dynamic optimization threshold method to obtain a more complete
moving object. And then, morphological filtering is introduced to eliminate the noise and solve the background
disturbance problem. At last, contour projection analysis is combined with the shape analysis to remove the effect of
shadow, the moving human body are accurately and reliably detected. The experiment results show that the proposed
method runs quickly, accurately and fits for the real-time detection.
Title :Image compression using image inpainting
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/image-compression-image-inpainting
Abstract : This paper introduces a novel examplar-based inpainting algorithm through investigating the sparsity of
natural image patches. Two novel concepts of sparsity at the patch level are proposed for modeling the patch priority
and patch representation, which are two crucial steps for patch propagation in the examplar-based inpainting
approach. First, patch structure sparsity is designed to measure the confidence of a patch located at the image
structure (e.g., the edge or corner) by the sparseness of its nonzero similarities to the neighboring patches. The patch
with larger structure sparsity will be assigned higher priority for further inpainting. Second, it is assumed that the patch
to be filled can be represented by the sparse linear combination of candidate patches under the local patch

consistency constraint in a framework of sparse representation. Compared with the traditional examplar-based
inpainting approach, structure sparsity enables better discrimination of structure and texture, and the patch sparse
representation forces the newly inpainted regions to be sharp and consistent with the surrounding textures.
Experiments on synthetic and natural images show the advantages of the proposed approach.
Title :Brain Tumor Detection from Pre-Processed MR Images using Segmentation Techniques
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/brain-tumor-detection-pre-processed-mr-images-segmentation
Abstract : Magnetic resonance imaging (MRI) has become a common way to study brain tumor. In this paper we
pre-process the two-dimensional magnetic resonance images of brain and subsequently detect the tumor using edge
detection technique and color based segmentation algorithm. Edge-based segmentation has been implemented using
operators e.g. Sobel, Prewitt, Canny and Laplacian of Gaussian operators. The color-based segmentation method
has been accomplished using K-means clustering algorithm. The color-based segmentation carefully selects the
tumor from the pre-processed image as a clustering feature. The present work demonstrates that the method can
successfully detect the brain tumor and thereby help the doctors for analyzing tumor size and region. The algorithms
have been developed on MATLAB version 7.6.0 (R2008a) platform.
Title :Multiscale Modeling for Image Analysis of Brain Tumor Studies
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/multiscale-modeling-image-analysis-brain-tumor-studies
Abstract : Image-based modeling of tumor growth combines methods from cancer simulation and medical imaging.
In this context, we present a novel approach to adapt a healthy brain atlas to MR images of tumor patients. In order to
establish correspondence between a healthy atlas and a pathologic patient image, tumor growth modeling in
combination with registration algorithms is employed. In a first step, the tumor is grown in the atlas based on a new
multiscale, multiphysics model including growth simulation from the cellular level up to the biomechanical level,
accounting for cell proliferation and tissue deformations. Large-scale deformations are handled with an Eulerian
approach for finite element computations, which can operate directly on the image voxel mesh. Subsequently, dense
correspondence between the modified atlas and patient image is established using nonrigid registration. The method
offers opportunities in atlas-based segmentation of tumor- bearing brain images as well as for improved
patient-specific simulation and prognosis of tumor progression.
Title :Automatic Skin Lesion Segmentation via Iterative Stochastic Region Merging
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/automatic-skin-lesion-segmentation-iterative-stochastic-region-merging
Abstract : An automatic method for segmenting skin lesions in conventional macroscopic images is presented. The
images are acquired with conventional cameras, without the use of a dermoscope. Automatic segmentation of skin
lesions from macroscopic images is a very challenging problem due to factors such as illumination variations, irregular
structural and color variations, the presence of hair, as well as the occurrence of multiple unhealthy skin regions. To
address these factors, a novel iterative stochastic region-merging approach is employed to segment the regions
corresponding to skin lesions from the macroscopic images, where stochastic region merging is initialized first on a
pixel level, and subsequently on a region level until convergence. A region merging likelihood function based on the
regional statistics is introduced to determine the merger of regions in a stochastic manner. Experimental results show
that the proposed system achieves overall segmentation error of under 10% for skin lesions in macroscopic images,
which is lower than that achieved by existing methods.
Title :Adaptive Spectral Transform for Wavelet-Based Color Image Compression
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/adaptive-spectral-transform-wavelet-based-color-image-compression
Abstract : Since different regions of a color image generally exhibit different spectral characteristics, the energy

compaction of applying a single spectral transform to all regions is largely inefficient from a compression perspective.
Thus, it is proposed that different subsets of wavelet coefficients of a color image be subjected to different spectral
transforms before the resultant coefficients are coded by an efficient wavelet coefficient coding scheme such as that
used in JPEG2000 or color set partitioning in hierarchical trees (CSPIHT). A quad tree represents the spatial
partitioning of the set of high frequency coefficients of the color planes into spatially oriented subsets which may be
further partitioned into smaller directionally oriented subsets. The partitioning decisions and decisions to employ
fixed or signal-dependent bases for each subset are rate-distortion (R-D) optimized by employing a known analytical
R-D model for these coefficient coding schemes. A compression system of asymmetric complexity, that integrates the
proposed adaptive spectral transform with the CSPIHT coefficient coding scheme yields average coding gains of 0.3
dB and 0.9 dB in the Y component at 1.0 b/p and 2.5 b/p, respectively, and 0.9 dB and 1.35 dB in the U and V
components at 1.0 b/p and 2.5 b/p, respectively, over a reference compression system that integrates the single
spectral transform derived from the entire image with the CSPIHT coefficient coding scheme.
Title :Image Segmentation and Shape Analysis for Road-Sign Detection
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/image-segmentation-shape-analysis-road-sign-detection
Abstract : This paper proposes an automatic road-sign recognition method based on image segmentation and joint
transform correlation (JTC) with the integration of shape analysis. The presented system is universal, which is able to
detect traffic signs of any countries with any color and any of the existing shapes (e.g., circular, rectangular,
triangular, pentagonal, and octagonal) and is invariant to transformation (e.g., translation, rotation, scale, and
occlusion). The main contributions of this paper are: 1) the formulation of two new criteria for analyzing different
shapes using two basic geometric properties, 2) the recategorization of the rectangular signs into diamond or
nondiamond shapes based on the inclination of the four sides with the ground and 3) the employment of the
distortion-invariant fringe-adjusted JTC (FJTC) technique for recognition. There are three main stages in the proposed
algorithm: 1) segmentation by clustering the pixels based on the color features to find the regions of interest (ROIs);
2) traffic-sign detection by using two novel shape classification criteria, i.e., the relationship between area and
perimeter and the number of sides of a given shape; and 3) recognition of the road sign using FJTC to match the
unknown signs with the known reference road signs stored in the database. Experimental results on real-life images
show a high success rate and a very low false hit rate and demonstrate that the proposed framework is invariant to
translation, rotation, scale, and partial occlusions.
Title :Bag-of-Features Based Medical Image Retrieval via Multiple Assignment and Visual Words Weighting
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/bag-features-based-medical-image-retrieval-visual-words-weighting
Abstract : Bag-of-features based approaches have become prominent for image retrieval and image classification
tasks in the past decade. Such methods represent an image as a collection of local features, such as image patches
and key points with scale invariant feature transform (SIFT) descriptors. To improve the bag-of-features methods, we
first model the assignments of local descriptors as contribution functions, and then propose a novel multiple
assignment strategy. Assuming the local features can be reconstructed by their neighboring visual words in a
vocabulary, reconstruction weights can be solved by quadratic programming. The weights are then used to build
contribution functions, resulting in a novel assignment method, called quadratic programming (QP) assignment. We
further propose a novel visual word weighting method. The discriminative power of each visual word is analyzed by
the sub-similarity function in the bin that corresponds to the visual word. Each sub-similarity function is then treated as
a weak classifier. A strong classifier is learned by boosting methods that combine those weak classifiers. The
weighting factors of the visual words are learned accordingly. We evaluate the proposed methods on medical image
retrieval tasks. The methods are tested on three well-known data sets, i.e., the Image CLEFmed data set, the 304 CT
Set, and the basal-cell carcinoma image set. Experimental results demonstrate that the proposed QP assignment
outperforms the traditional nearest neighbor assignment, the multiple assignment, and the soft assignment, whereas
the proposed boosting based weighting strategy outperforms the state-of-the-art weighting methods, such as the term
frequency weights and the term frequency-inverse document frequency weights.
Title :Bi-Level Image Compression Estimating the Markov Order of Dependencies
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/bi-level-image-compression-estimating-markov-order-dependencies
Abstract : This paper presents a bi-level image compression method based on chain codes and entropy coders.
However, the proposed method also includes an order estimation process to estimate the order of dependencies that

may exist among the chain code symbols prior to the entropy coding stage. For each bi-level image, the method first
obtains its chain code representation and then estimates its order of symbol dependencies. This order value is used
to find the conditional and joint symbol probabilities corresponding to our newly defined Markov model. Our order
estimation process is based on the Bayesian information criterion (BIC), a statistically based model selection
technique that has proved to be a consistent order estimator. In our experiments, we show how our order estimation
process can help achieve more efficient compression levels by providing comparisons against some of the most
commonly used image compression standards such as the Graphics Interchange Format (GIF), Joint Bi-level Image
Experts Group (JBIG), and JBIG2.
Title :Supervised Spectral-Spatial Hyperspectral Image Classification With Weighted Markov Random Fields
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/supervised-spectral-spatial-hyperspectral-image-classification-with-weighted-mark
Abstract : This paper presents a new approach for hyperspectral image classification exploiting spectral-spatial
information. Under the maximum a posteriori framework, we propose a supervised classification model which includes
a spectral data fidelity term and a spatially adaptive Markov random field (MRF) prior in the hidden field. The data
fidelity term adopted in this paper is learned from the sparse multinomial logistic regression (SMLR) classifier, while
the spatially adaptive MRF prior is modeled by a spatially adaptive total variation (SpATV) regularization to enforce a
spatially smooth classifier. To further improve the classification accuracy, the true labels of training samples are fixed
as an additional constraint in the proposed model. Thus, our model takes full advantage of exploiting the spatial and
contextual information present in the hyperspectral image. An efficient hyperspectral image classification algorithm,
named SMLR-SpATV, is then developed to solve the final proposed model using the alternating direction method of
multipliers. Experimental results on real hyperspectral data sets demonstrate that the proposed approach outperforms
many state-of-the-art methods in terms of the overall accuracy, average accuracy, and kappa (k) statistic.
Title :Reversible Image Data Hiding with Contrast Enhancement
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/reversible-image-data-hiding-contrast-enhancement
Abstract : In this letter, a novel reversible data hiding (RDH) algorithm is proposed for digital images. Instead of trying
to keep the PSNR value high, the proposed algorithm enhances the contrast of a host image to improve its visual
quality. The highest two bins in the histogram are selected for data embedding so that histogram equalization can be
performed by repeating the process. The side information is embedded along with the message bits into the host
image so that the original image is completely recoverable. The proposed algorithm was implemented on two sets of
images to demonstrate its efficiency. To our best knowledge, it is the first algorithm that achieves image contrast
enhancement byRDH. Furthermore, the evaluation results show that the visual quality can be preserved after a
considerable amount of message bits have been embedded into the contrast-enhanced images, even better than
three specificMATLAB functions used for image contrast enhancement.
Title :An Efficient MRF Embedded Level Set Method for Image Segmentation
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/mrf-embedded-level-set-method-image-segmentation
Abstract : This paper presents a fast and robust level set method for image segmentation. To enhance the
robustness against noise, we embed a Markov random field (MRF) energy function to the conventional level set
energy function. This MRF energy function builds the correlation of a pixel with its neighbors and encourages them to
fall into the same region. To obtain a fast implementation of the MRF embedded level set model, we explore algebraic
multigrid (AMG) and sparse field method (SFM) to increase the time step and decrease the computation domain,
respectively. Both AMG and SFM can be conducted in a parallel fashion, which facilitates the processing of our
method for big image databases. By comparing the proposed fast and robust level set method with the standard level
set method and its popular variants on noisy synthetic images, synthetic aperture radar (SAR) images, medical
images and natural images, we comprehensively demonstrate the new method is robust against various kinds of
noises. Especially, the new level set method can segment an image of size 500 by 500 within three seconds on
MATLAB R2010b installed in a computer with 3.30GHz CPU and 4GB memory.
Title :Image Sensor-Based Heart Rate Evaluation From Face Reflectance Using Hilbert-Huang Transform
Language : Matlab

Project Link :
http://kasanpro.com/p/matlab/heart-rate-evaluation-from-face-reflectance-using-hilbert-huang-transform
Abstract : Monitoring heart rates using conventional electrocardiogram equipment requires patients to wear adhesive
gel patches or chest straps that can cause skin irritation and discomfort. Commercially available pulse oximetry
sensors that attach to the fingertips or earlobes also cause inconvenience for patients and the spring-loaded clips can
be painful to use. Therefore, a novel robust face-based heart rate monitoring technique is proposed to allow for the
evaluation of heart rate variation without physical contact with the patient. Face reflectance is first decomposed from a
single image and then heart rate evaluation is conducted from consecutive frames according to the periodic variation
of reflectance strength resulting from changes to hemoglobin absorptivity across the visible light spectrum as
heartbeats cause changes to blood volume in the blood vessels in the face. To achieve a robust evaluation, ensemble
empirical mode decomposition of the Hilbert-Huang transform is used to acquire the primary heart rate signal while
reducing the effect of ambient light changes. Our proposed approach is found to outperform the current state of the
art, providing greater measurement accuracy with smaller variance and is shown to be feasible in real-world
environments.
Title :Fusion-Based Restoration of The Underwater Images
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/fusion-based-restoration-the-underwater-images
Abstract : In this paper we introduce a novel strategy that effectively enhance the visibility of underwater images. Our
method is build-up on the fusion strategy that takes a sequence of inputs derived from the initial image. Practically,
our fusion-based method aims to yield a final image that overcomes the deficiencies existing in the degraded input
images by employing several weight maps that discriminate the regions characterized by poor visibility. The extensive
experiments demonstrate the utility of our solution since the visibility range of the underwater images is significantly
increased by improving both the scene contrast and the color appearance.
Title :Image Registration By Region Cluster SIFT Matching
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/image-registration-by-region-cluster-sift-matching
Abstract : Multi sensor image registration becoming a challenge task due to the poor performance of there sensitivity
in scale, intensity variation and distortion. In this paper an optimized region cluster SIFT technique is used to image
registration. This technique has five phases. In the first phase Scale Invariant Feature Transform (SIFT) is applied to
extract key points in the referenced image. In the second phase, reference image segmented by regions by color
based segmentation approach, these are called clusters in the reference image. In the third phase difference of
Gaussian (DoG) filter is applied and key points with low contrast, localed at edge are discarderd. The fourth phase is
the matching phase, to achieve the distortion invariant or resolution invariant registration, key points are matched
according to the clusters in both referenced image and target image. Finally the fourth phase is the piece wise
transformation is applied to set the resultant image.
Title :Image Registration By Maximal Planar Graph
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/image-registration-by-maximal-planar-graph
Abstract : Multi sensor image registration becoming a challenge task due to the poor performance of think sensitivity
in scale, intensity variation and distortion. In this paper SIFT technique is used to image registration. This technique
has five phases. In the first phase Scale Invariant Feature Transform (SIFT) is applied to extract key points in the
referenced image. In the second phase, reference image segmented by regions by color segmentation approach, In
the third phase an maximal planer graph is constructed by region adjacency. In the fourth step loaded key points are
re ducted by comparing with maximal element graph lines in points with distance less than a three old with the
nearest graph edge are included and other points are discarded. Finally the fourth phase is the piece wise
transformation is applied to set the resultant image.
Title :Image Segmentation and Shape Analysis for Road-Sign Detection
Language : C#
Project Link : http://kasanpro.com/p/c-sharp/image-segmentation-shape-analysis-road-sign-detection-code

Abstract : This paper proposes an automatic road-sign recognition method based on image segmentation and joint
transform correlation (JTC) with the integration of shape analysis. The presented system is universal, which is able to
detect traffic signs of any countries with any color and any of the existing shapes (e.g., circular, rectangular,
triangular, pentagonal, and octagonal) and is invariant to transformation (e.g., translation, rotation, scale, and
occlusion). The main contributions of this paper are: 1) the formulation of two new criteria for analyzing different
shapes using two basic geometric properties, 2) the recategorization of the rectangular signs into diamond or
nondiamond shapes based on the inclination of the four sides with the ground and 3) the employment of the
distortion-invariant fringe-adjusted JTC (FJTC) technique for recognition. There are three main stages in the proposed
algorithm: 1) segmentation by clustering the pixels based on the color features to find the regions of interest (ROIs);
2) traffic-sign detection by using two novel shape classification criteria, i.e., the relationship between area and
perimeter and the number of sides of a given shape; and 3) recognition of the road sign using FJTC to match the
unknown signs with the known reference road signs stored in the database. Experimental results on real-life images
show a high success rate and a very low false hit rate and demonstrate that the proposed framework is invariant to
translation, rotation, scale, and partial occlusions.
Title :Automatic Image Registration using SIFT-NCC
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/automatic-image-registration-sift-ncc
Abstract : Accurate, robust and automatic image registration is critical task in many typical applications that employ
multi-sensor and/or multi-date imagery information. The main content of this paper is an algorithm for the registration
of digital images. Some multi-sensed or temporal images contain large number of speckles and noise, or image can
have some distortion by some means. For these reasons, we need to remove the noises, speckle and to recover from
distortion. We register two to find the similarity between the images. This paper discusses techniques for image
registration based on SIFT. In this proposed framework we use NCC metrics for optimizing the matching work. Best
bin first search using kd tree is used for feature matching and RANSAC is used for outlier elimination.
Title :Outdoor Scene Image Segmentation Based on Background Recognition and Perceptual Organization
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/outdoor-scene-image-segmentation-based-background-recognition
Abstract : In this paper, we propose a novel outdoor scene image segmentation algorithm based on background
recognition and perceptual organization. We recognize the background objects such as the sky, the ground, and
vegetation based on the color and texture information. For the structurally challenging objects, which usually consist
of multiple constituent parts, we developed a perceptual organization model that can capture the nonacci- dental
structural relationships among the constituent parts of the structured objects and, hence, group them together
accordingly without depending on a priori knowledge of the specific objects. Our experimental results show that our
proposed method outper- formed two state-of-the-art image segmentation approaches on two challenging outdoor
databases (Gould data set and Berkeley segmentation data set) and achieved accurate segmentation quality on
various outdoor natural scene environments.
Title :Optimal Design of a Tilling Machine Reduction Gearbox Using Matlab
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/tilling-machine-reduction-gearbox
Abstract : This paper describes the optimal design of the reduction gearbox of a tillage machine. The minimum
center diameter was selected as the objective, and the contact fatigue strength, bending fatigue strength, condition of
nonintervention, and oil film thickness ratio of the gearbox were applied as constraint conditions. The optimal model
was solved by a Matlab program. The results show that the center diameter of the reduction gearbox decreased by
about 10%. The resulting decrease in weight and volume led to a reduction in the amount of gearbox material and a
consequent decrease in production cost.
Title :Multimodal Analysis for Identification and Segmentation of Moving-Sounding Objects

Language : Matlab
Project Link :
http://kasanpro.com/p/matlab/multimodal-analysis-identification-segmentation-moving-sounding-objects
Abstract : In this paper, we propose a novel method that exploits correlation between audio-visual dynamics of a
video to segment and localize objects that are the dominant source of audio. Our approach consists of a two-step
spatiotemporal segmentation mechanism that relies on velocity and acceleration of moving objects as visual features.
Each frame of the video is segmented into regions based on motion and appearance cues using the QuickShift
algorithm, which are then clustered over time using K-means, so as to obtain a spatiotemporal video segmentation.
The video is represented by motion features computed over individual segments. The Mel-Frequency Cepstral
Coefficients (MFCC) of the audio signal, and their first order derivatives are exploited to represent audio. The
proposed framework assumes there is a non-trivial correlation between these audio features and the velocity and
acceleration of the moving and sounding objects. The canonical correlation analysis (CCA) is utilized to identify the
moving objects which are most correlated to the audio signal. In addition to moving-sounding object identification, the
same framework is also exploited to solve the problem of audio-video synchronization, and is used to aid interactive
segmentation. We evaluate the performance of our proposed method on challenging videos. Our experiments
demonstrate significant increase in performance over the state-of-the-art both qualitatively and quantitatively, and
validate the feasibility and superiority of our approach.
Title :Efficient Reversible Watermarking Based on Adaptive Prediction-Error Expansion and Pixel Selection
Language : Matlab
Project Link :
http://kasanpro.com/p/matlab/efficient-reversible-watermarking-based-adaptive-prediction-error-expansion
Abstract : Prediction-error expansion (PEE) is an important technique of reversible watermarking which can embed
large payloads into digital images with low distortion. In this paper, the PEE technique is further investigated and an
efficient reversible watermarking scheme is proposed, by incorporating in PEE two new strategies, namely, adaptive
embedding and pixel selection. Unlike conventional PEE which embeds data uniformly, we propose to adaptively
embed 1 or 2 bits into expandable pixel according to the local complexity. This avoids expanding pixels with large
prediction-errors, and thus, it reduces embedding impact by decreasing the maximum modification to pixel values.
Meanwhile, adaptive PEE allows very large payload in a single embedding pass, and it improves the capacity limit of
conventional PEE. We also propose to select pixels of smooth area for data embedding and leave rough pixels
unchanged. In this way, compared with conventional PEE, a more sharply distributed prediction-error histogram is
obtained and a better visual quality of watermarked image is observed. With these improvements, our method outper-
forms conventional PEE. Its superiority over other state-of-the-art methods is also demonstrated experimentally.
Title :Efficient Generalized Integer Transform for Reversible Watermarking
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/efficient-generalized-integer-transform-reversible-watermarking
Abstract : In this letter, an efficient integer transform based reversible watermarking is proposed. We first show that
Tian's difference expansion (DE) technique can be reformulated as an integer transform. Then, a generalized integer
transform and a payload-dependent location map are constructed to extend the DE technique to the pixel blocks of
arbitrary length. Meanwhile, the distortion can be controlled by preferentially selecting embeddable blocks that
introduce less distortion. Finally, the superiority of the proposed method is experimental verified by comparing with
other existing schemes.
Title :Reversible Image Watermarking Using Interpolation Technique
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/reversible-image-watermarking-using-interpolation-technique
Abstract : Watermarking embeds information into a digital signal like audio, image, or video. Reversible image
watermarking can restore the original image without any distortion after the hidden data is extracted. In this paper, we
present a novel reversible watermarking scheme using an interpolation technique, which can embed a large amount
of covert data into images with imperceptible modification. Different from previous watermarking schemes, we utilize
the interpolation-error, the difference between interpolation value and corresponding pixel value, to embed bit "1" or
"0" by expanding it additively or leaving it unchanged. Due to the slight modification of pixels, high image quality is

preserved. Exper- imental results also demonstrate that the proposed scheme can provide greater payload capacity
and higher image fidelity compared with other state-of-the-art schemes.
Title :License Plate Character Recognition System using Neural Network
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/license-plate-character-recognition-system-neural-network
Abstract : Intelligent Transportation System (ITS) has become an integral part of the Transportation Industry these
days and it consists of License Plate Recognition (LPR) System. License Plate Recognition is also called Car Plate
Recognition (CPR) or Automatic Number Plate Recognition (ANPR) System. In LPR System, when a vehicle steps
over magnetic loop detector it senses car and takes image of the car, following image preprocessing operations for
improvement in the quality of car image. From this enhanced image, license plate region is recognized and extracted.
Then character fragmentation/segmentation is performed on extracted License Plate and these segmented characters
are recognized using Neural Network in this paper.
Title :License Plate Recognition System using Visual Words
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/license-plate-recognition-system-visual-words
Abstract :
Title :Reconstruction of Underwater Image by Bispectrum
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/reconstruction-underwater-image-bispectrum
Abstract : Reconstruction of an underwater object from a sequence of images distorted by moving water waves is a
challenging task. A new approach is presented in this paper. We make use of the bispectrum technique to analyze the
raw image sequences and recover the phase information of the true object. We test our approach on both simulated
and real-world data, sepa- rately. Results show that our algorithm is very promising. Such technique has wide
applications to areas such as ocean study and submarine observation.
Title :Visually Lossless Encoding for JPEG2000
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/visually-lossless-encoding-jpeg2001
Abstract : Due to exponential growth in image sizes, visually lossless coding is increasingly considered as an
alternative to numerically lossless coding, which has limited compression ratios. This paper presents a method of
encoding color images in a visually lossless manner using JPEG2000. In order to hide coding artifacts caused by
quantization, visibility thresholds (VTs) are measured and used for quantization of subbands in JPEG2000. The VTs
are experimentally determined from statistically modeled quantization distortion, which is based on the distribution of
wavelet coefficients and the dead-zone quantizer of JPEG2000. The resulting VTs are adjusted for locally changing
backgrounds through a visual masking model, and then used to determine the minimum number of coding passes to
be included in the final codestream for visually lossless quality under the desired viewing conditions. Codestreams
produced by this scheme are fully JPEG2000 Part-I compliant.
Title :2D Image Morphing using Pixels based Color Transition Methods
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/morphing-2d-image-pixels-based-color-transition-me...
Abstract : Image morphing is the construction of an image sequence depicting a gradual transition between two
images, has been extensively investigated now a days. 2D image morphing adds animations to the silent photographs
which generally communicate limited information. The color transition method used in image morphing decides the
quality of the intermediate images generated by controlling the color blending rate. If the color blending is done

uniformly throughout the morphing process, good morph sequence is generated. Morph sequence has earlier morphs
similar to source and last morphs similar to the target image. The middle image in the entire morph sequence is
neither source nor the target image. Hence the quality of morphs depends on the quality of middle images. If it look
good then entire sequence looks good. In this paper methods of color transition by averaging the pixels and by
merging the color difference between pixels are proposed. The later one generates better quality middle image and
entire morph sequence than most commonly used cross dissolve method of color transition.
Title :2D Image Morphing using Pixels based Color Transition Methods
Language : C#
Project Link : http://kasanpro.com/p/c-sharp/morphing-2d-image-pixels-based-color-transition
Abstract : Image morphing is the construction of an image sequence depicting a gradual transition between two
images, has been extensively investigated now a days. 2D image morphing adds animations to the silent photographs
which generally communicate limited information. The color transition method used in image morphing decides the
quality of the intermediate images generated by controlling the color blending rate. If the color blending is done
uniformly throughout the morphing process, good morph sequence is generated. Morph sequence has earlier morphs
similar to source and last morphs similar to the target image. The middle image in the entire morph sequence is
neither source nor the target image. Hence the quality of morphs depends on the quality of middle images. If it look
good then entire sequence looks good. In this paper methods of color transition by averaging the pixels and by
merging the color difference between pixels are proposed. The later one generates better quality middle image and
entire morph sequence than most commonly used cross dissolve method of color transition.
Title :Underwater Image Enhancement based on Wavelet Decomposition
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/underwater-image-enhancement-based-wavelet-decomposition
Abstract :
Title :An Enhanced Bag of Visual Word Vector Space Model to Represent Visual Content in Athletics Images
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/bag-visual-word-vector-space-model-visual-content-athletics-images
Abstract : Images that have a different visual appearance may be semantically related using a higher level
conceptualization. However, image classification and retrieval systems tend to rely only on the low-level visual
structure within images. This paper presents a framework to deal with this semantic gap limitation by exploiting the
well-known bag-of-visual words (BVW) to represent visual content. The novelty of this paper is threefold. First, the
quality of visual words is improved by constructing visual words from representative keypoints. Second, domain
specific 'non-informative visual words' are detected which are useless to represent the content of visual data but
which can degrade the categorization capability. Distinct from existing frameworks, two main characteristics for
non-informative visual words are defined: a high document frequency (DF) and a small statistical association with all
the concepts in the collection. The third contribution in this paper is that a novel method is used to restructure the
vector space model of visual words with respect to a structural ontology model in order to resolve visual synonym and
polysemy problems. The experimental results show that our method can disambiguate visual word senses effectively
and can significantly improve classification, interpretation, and retrieval performance for the athletics images.
Title :A New Approach to Image Compression Using Vector Quantization of Wavelet Coefficients
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/image-compression-using-vector-quantization-wavelet-coefficients
Abstract : Traditional image coding methods, such as vector quantization (VQ), discrete cosine transform (DCT)
based coding, and entropy coding of subband, have been designed to eliminate statistical redundancy within still
images. In this paper, a combined approach utilizing both transform coding and vector quantization techniques is
used, hoping to achieve the best result in terms of compression ratio with acceptable recovery quality. The transform
coding used is 2-D wavelet transform and the key is to tap the correlation between wavelet coefficients of different
subbands in the same spatial location rather than only in the same orientation. Performance comparisons are made
with three other VQ-based compression models. The result shows the strength of this novel approach in that it has
the best reconstructed image quality in terms of its signal to noise ratio for a fixed compression ratio.

Title :FABRIC DEFECT DETECTION USING MULTI-LEVEL TUNED-MATCHED GABOR FILTERS
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/fabric-defect-detection-using-multi-level-tuned-matched-gabor-filters
Abstract : This paper proposes a new defect detection scheme for woven fabrics. The proposed scheme is divided
into two parts, namely the training part and the defect detection part. In the training part, a non-defective fabric image
is used as a template image, and a finite set of multi-level Gabor wavelets are tuned to match the texture information
of the image. In the defect detection part, filtered images from different levels are fused together and the constructed
detection scheme is used to detect defects in fabric sample images with the same texture background as that of the
template image. A filter selection method is also developed to select optimal filters to facilitate defect detection. The
novelty of the method comes from the observation that a Gabor filter with finer resolutions than the fabric defects yarn
can contribute very little for defect segmentation but need additional computational time. The proposed scheme is
tested by using 78 homogeneous textile fabric images. The results exhibit accurate defect detections with lower
false alarms than using the standard Gabor wavelets. Analysis of the computational complexity of the proposed
detection scheme is derived, which shows that the scheme can be implemented in real time easily.
Title :Video Watermarking Scheme Based on Principal Component Analysis and Wavelet Transform
Language : Matlab
Project Link :
http://kasanpro.com/p/matlab/video-watermarking-scheme-based-principal-component-analysis-wavelet-transform
Abstract : This paper presents a novel technique for embedding a binary logo watermark into video frames. The
proposed scheme is an imperceptible and a robust hybrid video watermarking scheme. PCA is applied to each block
of the two bands (LL - HH) which result from Discrete Wavelet transform of every video frame. The watermark is
embedded into the principal components of the LL blocks and HH blocks in different ways. Combining the two
transforms improved the performance of the watermark algorithm. The scheme is tested by applying various attacks.
Experimental results show no visible difference between the watermarked frames and the original frames and show
the robustness against a wide range of attacks such as MPEG coding, JPEG coding, Gaussian noise addition,
histogram equalization, gamma correction, contrast adjustment, sharpen filter, cropping, resizing, and rotation.
Title :Super-Resolution-based Inpainting
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/super-resolution-based-inpainting
Abstract : This paper introduces a new examplar-based inpainting framework. A coarse version of the input image
is first inpainted by a non- parametric patch sampling. Compared to existing approaches, some im-provements have
been done (e.g. filling order computation, combination of K nearest neighbours). The inpainted of a coarse version of
the input image allows to reduce the computational complexity, to be less sensitive to noise and to work with the
dominant orientations of image structures. From the low-resolution inpainted image, a single-image super-resolution
is applied to recover the details of missing areas. Experimental results on natural images and texture synthesis
demonstrate the effectiveness of the proposed method .
Title :Scalable Face Image Retrieval using Attribute-Enhanced Sparse Codewords
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/scalable-face-image-retrieval-attribute-enhanced-sparsewords
Abstract : Photos with people (e.g., family, friends, celebrities, etc.) are the major interest of users. Thus, with the
exponentially growing photos, large-scale content-based face image retrieval is an enabling technology for many
emerging applications. In this work, we aim to utilize automatically detected human attributes that contain semantic
cues of the face photos to improve content- based face retrieval by constructing semantic codewords for efficient
large-scale face retrieval. By leveraging human attributes in a scalable and systematic framework, we propose two
orthogonal methods named attribute-enhanced sparse coding and attribute- embedded inverted indexing to improve
the face retrieval in the offline and online stages. We investigate the effectiveness of different attributes and vital
factors essential for face retrieval. Experimenting on two public datasets, the results show that the proposed methods

can achieve up to 43.5% relative improvement in MAP compared to the existing methods.
Title :Multichannel Non-Local Means Fusion for Color Image Denoising
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/multichannel-non-local-means-fusion-color-image-denoising
Abstract : In this paper, we propose an advanced color image denoising scheme called multichannel non-local
means fusion (MNLF), where noise reduction is formulated as the minimization of a penalty function. An inherent
feature of color images is the strong inter-channel correlation, which is introduced into the penalty function as
additional prior constraints to expect a better performance. The optimal solution of the minimization problem is derived
consisting of constructing and fusing multiple non- local means (NLM) spanning all three channels. The weights in the
fusion are optimized to minimize the overall mean squared denoising error, with the help of the extended and adapted
Stein's unbiased risk estimator (SURE). Simulations on representative test images under various noise levels verify
the improvement brought by the multichannel NLM compared to the traditional single-channel NLM. Meanwhile,
MNLF provides competitive performance both in terms of the color peak signal-to-noise ratio (cPSNR) and in
perceptual quality when compared with other state-of-the-art benchmarks.
Title :Noise Reduction Based on Partial-Reference, Dual-Tree Complex Wavelet Transform Shrinkage
Language : Matlab
Project Link :
http://kasanpro.com/p/matlab/noise-reduction-based-partial-reference-dual-tree-complex-wavelet-transform-shrinkage
Abstract : This paper presents a novel way to reduce noise introduced or exacerbated by image enhancement
methods, in particular algorithms based on the random spray sampling technique, but not only. According to the
nature of sprays, output images of spray-based methods tend to exhibit noise with unknown statistical distribution. To
avoid inappropriate assumptions on the statistical characteristics of noise, a different one is made. In fact, the
non-enhanced image is considered to be either free of noise or affected by non-perceivable levels of noise. Taking
advantage of the higher sensitivity of the human visual system to changes in brightness, the analysis can be limited to
the luma channel of both the non-enhanced and enhanced image. Also, given the importance of directional content in
human vision, the analysis is performed through the dual-tree complex wavelet transform (DTWCT). Unlike the
discrete wavelet transform, the DTWCT allows for distinction of data directionality in the transform space. For each
level of the transform, the standard deviation of the non-enhanced image coefficients is computed across the six
orientations of the DTWCT, then it is normalized. The result is a map of the directional structures present in the
non-enhanced image. Said map is then used to shrink the coefficients of the enhanced image. The shrunk coefficients
and the coefficients from the non-enhanced image are then mixed according to data directionality. Finally, a
noise-reduced version of the enhanced image is computed via the inverse transforms. A thorough numerical analysis
of the results has been performed in order to confirm the validity of the proposed approach.
Title :Hyperspectral image noise reduction based on rank-1 tensor decomposition
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/hyperspectral-image-noise-reduction-based-rank-1-tensor-decomposition
Abstract : In this study, a novel noise reduction algorithm for hyperspectral imagery (HSI) is proposed based on
high-order rank-1 tensor decomposition. The hyperspectral data cube is considered as a three-order tensor that is
able to jointly treat both the spatial and spectral modes. Subsequently, the rank-1 tensor decomposition (R1TD)
algorithm is applied to the tensor data, which takes into account both the spatial and spectral information of the
hyperspectral data cube. A noise-reduced hyperspectral image is then obtained by combining the rank-1 tensors
using an eigenvalue intensity sorting and reconstruction technique. Compared with the existing noise reduction
methods such as the conventional channel-by-channel approaches and the recently developed multidimensional filter,
the spatial-spectral adaptive total variation filter, experiments with both synthetic noisy data and real HSI data reveal
that the proposed R1TD algorithm significantly improves the HSI data quality in terms of both visual inspection and
image quality indices. The subsequent image classification results further validate the effectiveness of the proposed
HSI noise reduction algorithm.
Title :Fuzzy C-Means Clustering with Local Information and Kernel Metric for Image Segmentation
Language : Matlab
Project Link :

http://kasanpro.com/p/matlab/fuzzy-c-means-clustering-local-information-kernel-metric-image-segmentation
Abstract : In this paper, we present an improved fuzzy C-means (FCM) algorithm for image segmentation by
introducing a tradeoff weighted fuzzy factor and a kernel metric. The tradeoff weighted fuzzy factor depends on the
space distance of all neighboring pixels and their gray-level difference simultaneously. By using this factor, the new
algorithm can accurately estimate the damping extent of neighboring pixels. In order to further enhance its robustness
to noise and outliers, we introduce a kernel distance measure to its objective function. The new algorithm adaptively
determines the kernel parameter by using a fast bandwidth selection rule based on the distance variance of all data
points in the collection. Furthermore, the tradeoff weighted fuzzy factor and the kernel distance measure are both
parameter free. Experimental results on synthetic and real images show that the new algorithm is effective and
efficient, and is relatively independent of this type of noise.
Title :Data Hiding in Motion Vectors of Compressed Video Based on Their Associated Prediction Error
Language : Matlab
Project Link :
http://kasanpro.com/p/matlab/data-hiding-motion-vectors-noise-video-based-their-associated-prediction-error
Abstract : This paper deals with data hiding in compressed video. Unlike data hiding in images and raw video which
operates on the images themselves in the spatial or transformed domain which are vulnerable to steganalysis, we
target the motion vectors used to encode and reconstruct both the forward predictive (P)-frame and bidirectional
(B)-frames in compressed video. The choice of candidate subset of these motion vectors are based on their
associated macro block prediction error, which is different from the approaches based on the motion vector attributes
such as the magnitude and phase angle, etc. A greedy adaptive threshold is searched for every frame to achieve
robustness while maintaining a low prediction error level. The secret message bit stream is embedded in the least
significant bit of both components of the candidate motion vectors. The method is implemented and tested for hiding
data in natural sequences of multiple groups of pictures and the results are evaluated. The evaluation is based on two
criteria: minimum distortion to the reconstructed video and minimum overhead on the compressed video size. Based
on the aforementioned criteria, the proposed method is found to perform well and is compared to a motion vector
attribute-based method from the literature.
Title :Hyperspectral Image Noise Reduction based on K-SVD Tensor Decomposition
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/hyperspectral-image-noise-reduction-based-k-svd-tensor-decomposition
Abstract : In this Paper,mixed noise reduction algorithm for hyperspectral imagery (HSI). The hyperspectral data
cube is considered as a three-order tensor that is able to jointly treat both the spatial and spectral modes.This entire
denoising process is based on the K-SVD denoising algorithm.Our work involved in minimization model to remove
mixed noise such as Gaussian-Gaussian mixture, impulse noise, and Gaussian-impulse noise from the HSI data. To
solve the weighted rank-one approximation problem arisen from the proposed model, a new iterative scheme is given
and the low rank approximation can be obtained by singular value decomposition (SVD, which takes into account both
the spatial and spectral information of the hyperspectral data cube and we present a new weighting data fidelity
function, which has the same minimizer as the original likelihood functional but is much easier to optimize. The
weighting function in the model can be determined by the algorithm itself, and it plays a role of noise detection in
terms of the different estimated noise parameters.
Title :Structural Texture Similarity Metrics for Image Analysis and Retrieval
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/structural-texture-similarity-metrics-image-analysis-retrieval
Abstract : We develop new metrics for texture similarity that accounts for human visual perception and the stochastic
nature of textures. The metrics rely entirely on local image statistics and allow substantial point-by-point deviations
between textures that according to human judgment are essentially identical. The proposed metrics extend the ideas
of structural similarity and are guided by research in texture analysis-synthesis. They are implemented using a
steerable filter decomposition and incorporate a concise set of subband statistics, computed globally or in sliding
windows. We conduct systematic tests to investigate metric performance in the context of "known-item search," the
retrieval of textures that are "identical" to the query texture. This eliminates the need for cumbersome subjective tests,
thus enabling comparisons with human performance on a large database. Our experimental results indicate that the
proposed metrics outperform peak signal-to-noise ratio (PSNR), structural similarity metric (SSIM) and its variations,

as well as state-of- the-art texture classification metrics, using standard statistical measures.
Title :Identification and Segmentation of Moving-Sounding Objects using Background Substraction
Language : Matlab
Project Link :
http://kasanpro.com/p/matlab/identification-segmentation-moving-sounding-objects-background-substraction
Abstract : In this paper, we propose a novel method for segmentation of moving and sounding objects by
investigating the maximum correlation between the audio and visual features. This paper presents a new algorithm for
detecting moving objects from a static background scene to detect moving object based on background subtraction.
We set up a reliable background updating model based on statistical. We use this motion regions as visual features
which are then grouped together, and for audio features we use MFCC and the first derivative of MFCC (MFCC_D) .
We assume that the velocity of objects is correlated to the MFCC features, while their acceleration is correlated to
MFCC_D. The maximum correlation is computed using canonical correlation (CCA) which is a method for finding the
maximum correlation between two random variables with different dimensionality.CCA can be considered as an
eigensystem problem. For an eigensystem to have a solution, enough samples are needed to estimate the statistics
of the signals. Since the correlation is usually analyzed over a small number of frames (i.e., number of samples), we
propose to represent audio and visual modalities at a higher level of abstraction. Video motion analysis concerns the
detection, tracking and recognition of moving behaviors, from image sequences. According to the result of moving
object detection research on video sequences.
Title :Building Change Detection Based on Satellite Stereo Imagery and Digital Surface Models
Language : Matlab
Project Link :
http://kasanpro.com/p/matlab/building-change-detection-based-satellite-stereo-imagery-digital-surface-model
Abstract : Building change detection is a major issue for urban area monitoring. Due to different imaging conditions
and sensor parameters, 2-D information delivered by satellite images from different dates is often not sufficient when
dealing with building changes. Moreover, due to the similar spectral characteristics, it is often difficult to distinguish
buildings from other man-made constructions, like roads and bridges, during the change detection procedure.
Therefore, stereo imagery is of importance to provide the height component which is very helpful in analyzing 3-D
building changes. In this paper, we propose a change detection method based on stereo imagery and digital surface
models (DSMs) generated with stereo matching methodology and provide a solution by the joint use of height
changes and Kullback-Leibler divergence similarity measure between the original images. The Dempster-Shafer
fusion theory is adopted to combine these two change indicators to improve the accuracy. In addition, vegetation and
shadow classifications are used as no-building change indicators for refining the change detection results. In the end,
an object-based building extraction method based on shape features is performed. For evaluation purpose, the
proposed method is applied in two test areas, one is in an industrial area in Korea with stereo imagery from the same
sensor and the other represents a dense urban area in Germany using stereo imagery from different sensors with
different resolutions. Our experimental results confirm the efficiency and high accuracy of the proposed methodology
even for different kinds and combinations of stereo images and consequently different DSM qualities.
Title :PCA Feature Extraction for Change Detection in Multidimensional Unlabelled Data
Language : Matlab
Project Link :
http://kasanpro.com/p/matlab/pca-feature-extraction-change-detection-multidimensional-unlabelled-data
Abstract : When classifiers are deployed in real world applications, it is assumed that the distribution of the incoming
data matches the distribution of the data used to train the classifier. This assumption is often incorrect, which
necessitates some form of change detection or adaptive classification. While there is a lot of research on change
detection based on the classification error, monitored over the course of the operation of the classifier, finding
changes in multidimensional unlabelled data is still a challenge. Here we propose to apply principal component
analysis (PCA) for feature extraction prior to the change detection. Supported by a theoretical example, we argue that
the components with the lowest variance should be retained as the extracted features because they are more likely to
be affected by a change. We chose a recently proposed semi-parametric log-likelihood change detection criterion
(SPLL) which is sensitive to changes in both mean and variance of the multidimensional distribution. An experiment
with 35 data sets and an illustration with a simple video segmentation demonstrate the advantage of using extracted
features compared to raw data. Further analysis shows that feature extraction through PCA is beneficial, specifically

for data with multiple balanced classes.
Title :Remote Sensing Image Segmentation by Combining Spectral and Texture Features
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/remote-sensing-image-segmentation-combining-spectral-texture-features
Abstract : We present a new method for remote sensing image segmentation, which utilizes both spectral and
texture information. Linear filters are used to provide enhanced spatial patterns. For each pixel location, we compute
combined spectral and texture features using local spectral histograms, which concatenate local histograms of all
input bands. We regard each feature as a linear combination of several representative features, each of which
corresponds to a segment. Segmentation is given by estimating combination weights, which indicate segment
ownership of pixels. We present segmentation solutions where representative features are either known or unknown.
We also show that feature dimensions can be greatly reduced via subspace projection. The scale issue is
investigated, and an algorithm is presented to automatically select proper scales, which does not require
segmentation at multiplescale levels. Experimental results demonstrate the promise of the proposed method.
Title :Garment Personalization via Identity Transfer
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/garment-personalization-identity-transfer
Abstract : We aim to create a more precise, natural clothing fit for users. We concentrate on a single image, striving
for high-quality results that create the experience of an identity transfer. The input to our system comprises a picture
of the system's user, called the user image, and a reference picture of a human model from a clothing catalog, called
the catalog image. Our system produces a real-time photo album depicting how users might look if they wore the
clothes and posed for a camera. One of our goals was to design a system that unskilled users could operate, in which
preprocessing of the user image and system training require only quick, simple interaction.
Title :Moving Object Detection with Background Model based on Spatio-Temporal Texture
Language : Matlab
Project Link :
http://kasanpro.com/p/matlab/moving-object-detection-background-model-based-spatio-temporal-texture
Abstract : Background subtraction is a common method for detecting moving objects, but it is yet a difficult problem
to distinguish moving objects from backgrounds when these backgrounds change significantly. Hence, we propose a
method for detecting moving objects with a background model that covers dynamic changes in backgrounds utilizing
a spatio-temporal texture named "Space-Time Patch", which describes motion and appearance, whereas
conventional textures describe appearance only. Our experimental results show the proposed method outperforms
one conventional method in three scenes: in an outdoor scene where leaves and branches of a tree are waving in
intermittent wind, in an indoor scene where ceiling lights are turned on and off frequently, and in an escalator scene
beside a window facing outdoors where some passengers are leaning over the hand-rail.
Title :Improvements of Driver Fatigue Detection System Based on Eye Tracking and Dynamic Template Matching
Language : Matlab
Project Link :
http://kasanpro.com/p/matlab/driver-fatigue-detection-system-based-eye-tracking-dynamic-template-matching
Abstract : Driver fatigue detection plays an important role in intelligent transportation systems for driving safety.
Therefore, it becomes an essential research issue these years. Recently, Horng and Chen proposed a real-time driver
fatigue detection system based on eye tracking and dynamic template matching. In their work, the driver fatigue
detection system consists of four parts: face detection, eye detection, eye tracking, and fatigue detection. However,
their work suffers from an exhaustive search in eye tracking with the conventional mean absolute difference (MAD)
matching function. To remedy the low accuracy in matching and inefficiency in search, in this paper, we first propose
two new matching functions, the edge map overlapping (EMO) and the edge pixel count (EPC), to enhance matching
accuracy. In addition, we utilize fast search algorithms, such as the 2D-log search and the three-step search
algorithms, to expedite search. The experimental results show that the 2D-log search with the EPC matching function
has the best performance on eye tracking; it only requires 22.29 search points on average to achieve 99.92% correct

rate of eye tracking, as comparing to the original work which requires 441 search points with only 96.01% correct rate.
By theoretical analysis, the total amount of computations for eye tracking in the 2D-log search with EPC only takes up
to about 10% of the original work. These improvements make the driver fatigue detection system more suitable for
implementations in embedded systems.
Title :Identification of Fault Types for Underground Cable using Discrete Wavelet Transform
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/identification-fault-types-underground-cable-discrete-wavelet-transform
Abstract : In this paper, a technique for identifying the phase with fault appearance in underground cable is
presented. The Wavelet transform has been employed to extract high frequency components superimposed on fault
signals simulated using ATP/EMTP. The coefficients obtained from the Wavelet transform are used in constructing a
decision algorithm. Various cases have been investigated so that the algorithm can be implemented. It is found that
the proposed method can indicate the fault types with satisfactory accuracy.
Title :Brain Tumor Detection using Neural Network
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/brain-tumor-detection-neural-network
Abstract : Medical image segmentation plays an important role in treatment planning, identifying tumors, tumor
volume, patient follow up and computer guided surgery. There are various techniques for medical image
segmentation. This paper presents a image segmentation technique for locating brain tumor(Astrocytoma- A type of
brain tumor).Proposed work has been divided in two phases-In the first phase MRI image database(Astrocytoma
grade I to IV) is collected and then preprocessing is done to improve quality of image. Second-phase includes three
steps-Feature extraction, Feature selection and Image segmentation. For feature extraction proposed work uses
GLCM (Grey Level co-occurrence matrix).To improve accuracy only a subset of feature is selected using hybrid
Genetic algorithm(Genetic Algorithm+fuzzy rough set) and based on these features fuzzy rules and membership
functions are defined for segmenting brain tumor from MRI images of .ANFIS is a adaptive network which combines
benefits of both fuzzy and neural network .Finally, a comparative analysis is performed between ANFIS, neural
network, Fuzzy ,FCM,K-NN, DWT+SOM,DWT+PCA+KN, Texture combined +ANN, Texture Combined+ SVM in
terms of sensitivity ,specificity ,accuracy.
Title :Adaptive Noise Reduction and Image Enhancment using MORPHOLOGICAL TRANSFORMATION
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/noise-reduction-image-enhancment-morphological-transformation
Abstract :
Title :Traffic Sign Recognition in Disturbing Environments
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/traffic-sign-recognition-disturbing-environments
Abstract : Traffic sign recognition is a difficult task if we aim at detecting and recognizing signs in images captured
from unfavorable environments. Complex background, weather, shadow, and other lighting-related problems may
make it difficult to detect and recognize signs in the rural as well as the urban areas. We employ discrete cosine
transform and singular value decomposition for ex-tracting features that defy external disturbances, and compare
different designs of detection and classification systems for the task. Experimental results show that our pilot systems
offer satisfactory performance when tested with very challenging data.
Title :Cartoon Plus Texture Image Inpainting using Coupled Variational Image Decomposition
Language : Java

Project Link :
http://kasanpro.com/p/java/cartoon-plus-texture-image-inpainting-coupled-variational-image-decomposition
Abstract : In this paper, we develop a decomposition model to inpainting problems. Our assumption is that the
underlying image is the superposition of cartoon and texture components. We use the total variation norm and its dual
norm to regularize the cartoon and texture, respectively. We recommend an efficient numerical algorithm based on
the splitting versions of augmented Lagrangian method to solve the problem. The proposed algorithm gives a
decomposition of cartoon and texture parts. These two parts can be further used in inpainting problems. Using the
decomposition, segemenation patches(High Resolution Patches) are defined. Filling order of the HR picture filling
order is computed on the HR picture with the sparsity-based method. The HR patch is then pasted into the missing
areas. However, as an overlap with the already synthesized areas is possible. Thus our work focus on implementation
of decomposition model and make inpainting at the missing pixels.
Title :Fingerprint Gender Classification using Wavelet Transform and Singular Value Decomposition
Language : Matlab
Project Link :
http://kasanpro.com/p/matlab/fingerprint-gender-classification-wavelet-transform-singular-value-decomposition
Abstract : A novel method of gender Classification from fingerprint is proposed based on discrete wavelet transform
(DWT) and singular value decomposition (SVD). The classification is achieved by extracting the energy computed
from all the sub-bands of DWT combined with the spatial features of non-zero singular values obtained from the SVD
of fingerprint images. K nearest neighbor (KNN) used as a classifier. This method is experimented with the internal
database of 3570 fingerprints finger prints in which 1980 were male fingerprints and 1590 were female fingerprints.
Fingerwise gender classification is achieved which is 94.32% for the left hand little fingers of female persons and
95.46% for the left hand index finger of male persons. Gender classification for any finger of male persons tested is
attained as 91.67% and 84.69% for female persons respectively. Overall classification rate is 88.28% has been
achieved.
Title :Brain Tumor Detection using Region based Iterative Reconstruction and Segmentation
Language : Java
Project Link : http://kasanpro.com/p/java/brain-tumor-detection-region-based-iterative-reconstruction-segmentation
Abstract : X-ray computed tomography (CT) is a powerful tool for noninvasive imaging of time-varying objects.
Identifiying tumors from the CT image is a chalanging one. In this paper we proposed a reconstruct method for CT
image and tumors are detected then using edge based segmentation algorithm.. In the past, methods have been
proposed to reconstruct images from continuously changing objects. For discretely or structurally changing objects,
however, such methods fail to reconstruct high quality images, mainly because assumptions about continuity are no
longer valid. In this paper, we propose a method to reconstruct structurally changing objects. Starting from the
observation that there exist regions within the scanned object that remain unchanged over time, we introduce an
iterative optimization routine that can automatically determine these regions and incorporate this knowledge in an
algebraic reconstruction method. And tumor detection was made from the reconstructed image.
Title :Automatic graph based approach for prior detection of diabetes and hypertension in retinal images
Language : Java
Project Link :
http://kasanpro.com/p/java/automatic-graph-based-prior-detection-diabetes-hypertension-retinal-images
Abstract : Retinal vessels are affected by several systemic diseases, namely diabetes, hypertension, and vascular
disorders. In diabetic retinopathy, the blood vessels often show abnormalities at early stages, as well as vessel
diameter alterations . Changes in retinal blood vessels, such as significant dilatation and elongation of main arteries,
veins, and their branches are also frequently associated with hypertension and other cardiovascular pathologies. The
classification of retinal vessels into artery/vein (A/V) is an important phase for automating the detection of vascular
changes, and for the calculation of characteristic signs associated with several systemic diseases such as diabetes,
hypertension, and other cardiovascular conditions. This paper presents an automatic approach for A/V classification
based on the analysis of a graph extracted from the retinal vasculature. The proposed method classifies the entire
vascular tree deciding on the type of each intersection point (graph nodes) and assigning one of two labels to each
vessel segment (graph links). Final classification of a vessel segment as A/V is performed through the combination of
the graph-based labeling results with a set of intensity features. The features were extracted, including exudates,

bifurcation angle, artery-to-veins diameter ratio, mean artery and veins diameters, form and size of optic disc, and
vessel tortuosity. And the identification of diabetes are made by the rule based conditions.
Title :Tumor Tissue Classification using Bayes and SVM Classifier
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/tumor-tissue-classification-bayes-svm-classifier
Abstract :
Title :A Compressive Sensing based Secure Watermark Detection and Privacy Preserving Storage Framework
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/secure-watermark-detection-privacy-preserving-storage-framework
Abstract : Privacy is a critical issue when the data owners outsource data storage or processing to a third party
computing service, such as the cloud. In this paper, we identify a cloud computing application scenario that requires
simultaneously performing secure watermark detection and privacy preserving multimedia data storage. We then
propose a compressive sensing (CS)-based framework using secure multiparty computation (MPC) protocols to
address such a requirement. In our framework, the multimedia data and secret watermark pattern are presented to
the cloud for secure watermark detection in a CS domain to protect the privacy. During CS transformation, the privacy
of the CS matrix and the watermark pattern is protected by the MPC protocols under the semi-honest security model.
We derive the expected watermark detection performance in the CS domain, given the target image, watermark
pattern, and the size of the CS matrix (but without the CS matrix itself). The correctness of the derived performance
has been validated by our experiments. Our theoretical analysis and experimental results show that secure watermark
detection in the CS domain is feasible. Our framework can also be extended to other collaborative secure signal
processing and data-mining applications in the cloud.
Title :A New Iterative Triclass Thresholding Technique in Image Segmentation
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/new-iterative-triclass-thresholding-technique-image-segmentation
Abstract : We present a new method in image segmentation that is based on Otsu's method but iteratively searches
for subregions of the image for segmentation, instead of treating the full image as a whole region for processing. The
iterative method starts with Otsu's threshold and computes the mean values of the two classes as separated by the
threshold. Based on the Otsu's threshold and the two mean values, the method separates the image into three
classes instead of two as the standard Otsu's method does. The first two classes are determined as the foreground
and background and they will not be processed further. The third class is denoted as a to-be-determined (TBD) region
that is processed at next iteration. At the succeeding iteration, Otsu's method is applied on the TBD region to
calculate a new threshold and two class means and the TBD region is again separated into three classes, namely,
foreground, background, and a new TBD region, which by definition is smaller than the previous TBD regions. Then,
the new TBD region is processed in the similar manner. The process stops when the Otsu's thresholds calculated
between two iterations is less than a preset threshold. Then, all the intermediate foreground and background regions
are, respectively, combined to create the final segmentation result. Tests on synthetic and real images showed that
the new iterative method can achieve better performance than the standard Otsu's method in many challenging
cases, such as identifying weak objects and revealing fine structures of complex objects while the added
computational cost is minimal.
Title :As-Projective-As-Possible Image Stitching with Moving DLT
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/as-projective-as-possible-image-stitching-moving-dlt
Abstract : We investigate projective estimation under model inadequacies, i.e., when the underpinning assumptions
of the projective model are not fully satisfied by the data. We focus on the task of image stitching which is customarily
solved by estimating a projective warp -- a model that is justified when the scene is planar or when the views differ
purely by rotation. Such conditions are easily violated in practice, and this yields stitching results with ghosting
artefacts that necessitate the usage of deghosting algorithms. To this end we propose as-projective-as-possible

warps, i.e., warps that aim to be globally projective, yet allow local non-projective deviations to account for violations
to the assumed imaging conditions. Based on a novel estimation technique called Moving Direct Linear
Transformation (Moving DLT), our method seamlessly bridges image regions that are inconsistent with the projective
model. The result is highly accurate image stitching, with significantly reduced ghosting effects, thus lowering the
dependency on post hoc deghosting.
Title :Captcha as Graphical Passwords--A New Security Primitive Based on Hard AI Problems
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/captcha-graphical-password
Abstract : Many security primitives are based on hard mathematical problems. Using hard AI problems for security is
emerging as an exciting new paradigm, but has been underexplored. In this paper, we present a new security
primitive based on hard AI problems, namely, a novel family of graphical password systems built on top of Captcha
technology, which we call Captcha as graphical passwords (CaRP). CaRP is both a Captcha and a graphical
password scheme. CaRP addresses a number of security problems altogether, such as online guessing attacks, relay
attacks, and, if combined with dual-view technologies, shoulder-surfing attacks. Notably, a CaRP password can be
found only probabilistically by automatic online guessing attacks even if the password is in the search set. CaRP also
offers a novel approach to address the well-known image hotspot problem in popular graphical password systems,
such as PassPoints, that often leads to weak password choices. CaRP is not a panacea, but it offers reasonable
security and usability and appears to fit well with some practical applications for improving online security.
Title :Corruptive Artifacts Suppression for Example-Based Color Transfer
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/corruptive-artifacts-suppression-example-based-color-transfer
Abstract : Example-based color transfer is a critical operation in image editing but easily suffers from some corruptive
artifacts in themapping process. In this paper,we propose a novel unified color transfer framework with corruptive
artifacts suppression, which performs iterative probabilistic color mapping with self-learning filtering scheme and
multiscale detail manipulation scheme inminimizing the normalized Kullback-Leibler distance. First, an iterative
probabilistic color mapping is applied to construct the mapping relationship between the reference and target images.
Then, a self-learning filtering scheme is applied into the transfer process to prevent from artifacts and extract details.
The transferred output and the extracted multi-levels details are integrated by the measurement minimization to yield
the final result. Our framework achieves a sound grain suppression, color fidelity and detail appearance seamlessly.
For demonstration, a series of objective and subjective measurements are used to evaluate the quality in color
transfer. Finally, a few extended applications are implemented to show the applicability of this framework.
Title :Fingerprint Compression Based on Sparse Representation
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/fingerprint-compression-based-sparse-representation
Abstract : A new fingerprint compression algorithm based on sparse representation is introduced. Obtaining an
overcomplete dictionary from a set of fingerprint patches allows us to represent them as a sparse linear combination
of dictionary atoms. In the algorithm, we first construct a dictionary for predefined fingerprint image patches. For a
new given fingerprint images, represent its patches according to the dictionary by computing l0-minimization and then
quantize and encode the representation. In this paper, we consider the effect of various factors on compression
results. Three groups of fingerprint images are tested. The experiments demonstrate that our algorithm is efficient
compared with several competing compression techniques (JPEG, JPEG 2000, andWSQ), especially at high
compression ratios. The experiments also illustrate that the proposed algorithm is robust to extract minutiae.
Title :How to Estimate the Regularization Parameter for Spectral Regression Discriminant Analysis and its Kernel
Version?
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/regularization-parameter-spectral-regression-discriminant-analysis
Abstract : Spectral regression discriminant analysis (SRDA) has recently been proposed as an efficient solution to
large-scale subspace learning problems. There is a tunable regularization parameter in SRDA, which is critical to

algorithm performance. However, how to automatically set this parameter has not been well solved until now. So this
regularization parameter was only set to be a constant in SRDA, which is obviously suboptimal. This paper proposes
to automatically estimate the optimal regularization parameter of SRDA based on the perturbation linear discriminant
analysis (PLDA). In addition, two parameter estimation methods for the kernel version of SRDA are also developed.
One is derived from the method of optimal regularization parameter estimation for SRDA. The other is to utilize the
kernel version of PLDA. Experiments on a number of publicly available databases demonstrate the effectiveness of
the proposed methods for face recognition, spoken letter recognition, handwritten digit recognition, and text
categorization.
Title :Image Classification Using Multiscale Information Fusion Based on Saliency Driven Nonlinear Diffusion Filtering
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/image-classification-based-fusion-diffusion-filtering
Abstract : In this paper, we propose saliency driven image multiscale nonlinear diffusion filtering. The resulting scale
space in general preserves or even enhances semantically important structures such as edges, lines, or flow-like
structures in the foreground, and inhibits and smoothes clutter in the background. The image is classified using
multiscale information fusion based on the original image, the image at the final scale at which the diffusion process
converges, and the image at a midscale. Our algorithm emphasizes the foreground features, which are important for
image classification. The background image regions, whether considered as contexts of the foreground or noise to the
foreground, can be globally handled by fusing information from different scales. Experimental tests of the
effectiveness of the multiscale space for the image classification are conducted on the following publicly available
datasets: 1) the PASCAL 2005 dataset; 2) the Oxford 102 flowers dataset; and 3) the Oxford 17 flowers dataset, with
high classification rates.
Title :LBP-Based Edge-Texture Features for Object Recognition
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/lbp-based-edge-texture-features-object-recognition
Abstract : This paper proposes two sets of novel edge-texture features, Discriminative Robust Local Binary Pattern
(DRLBP) and Ternary Pattern (DRLTP), for object recognition. By investigating the limitations of Local Binary Pattern
(LBP), Local Ternary Pattern (LTP) and Robust LBP (RLBP), DRLBP and DRLTP are proposed as new features.
They solve the problem of discrimination between a bright object against a dark background and vice-versa inherent
in LBP and LTP. DRLBP also resolves the problem of RLBP whereby LBP codes and their complements in the same
block are mapped to the same code. Furthermore, the proposed features retain contrast information necessary for
proper representation of object contours that LBP, LTP, and RLBP discard. Our proposed features are tested on
seven challenging data sets: INRIA Human, Caltech Pedestrian, UIUC Car, Caltech 101, Caltech 256, Brodatz, and
KTH-TIPS2- a. Results demonstrate that the proposed features outperform the compared approaches on most data
sets.
Title :Learning Layouts for Single-Page Graphic Designs
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/learning-layouts-single-page-graphic-designs
Abstract : This paper presents an approach for automatically creating graphic design layouts using a new
energy-based model derived from design principles. The model includes several new algorithm for analyzing graphic
designs, including the prediction of perceived importance, alignment detection, and hierarchical segmentation. Given
the model, we use optimization to synthesize new layouts for a variety of single-page graphic designs. Model
parameters are learned with Nonlinear Inverse Optimization (NIO) from a small number of example layouts. To
demonstrate our approach, we show result for application including generation design layouts in various styles,
retargeting designs to new sizes, and improving existing designs. We also compare our automatic results with
designs created using crowdsourcing and show that our approach performs slightly better than novice designers.
Title :Localization of License Plate Number Using Dynamic Image Processing Techniques And Genetic Algorithms
Language : Matlab
Project Link :
http://kasanpro.com/p/matlab/license-plate-number-localization-using-image-processing-and-genetic-algorithms

Abstract : In this research, a design of a new genetic algorithm (GA) is introduced to detect the locations of the
License Plate (LP) symbols. An adaptive threshold method has been applied to overcome the dynamic changes of
illumination conditions when converting the image into binary. Connected component analysis technique (CCAT) is
used to detect candidate objects inside the unknown image. A scale-invariant Geometric Relationship Matrix (GRM)
has been introduced to model the symbols layout in any LP which simplifies system adaptability when applied in
different countries. Moreover, two new crossover operators, based on sorting, have been introduced which greatly
improved the convergence speed of the system. Most of CCAT problems such as touching or broken bodies have
been minimized by modifying the GA to perform partial match until reaching to an acceptable fitness value. The
system has been implemented using MATLAB and various image samples have been experimented to verify the
distinction of the proposed system. Encouraging results with 98.4% overall accuracy have been reported for two
different datasets having variability in orientation, scaling, plate location, illumination and complex background.
Examples of distorted plate images were successfully detected due to the independency on the shape, color, or
location of the plate.
Title :Model-Based Edge Detector for Spectral Imagery Using Sparse Spatiospectral Masks
Language : Matlab
Project Link :
http://kasanpro.com/p/matlab/model-based-edge-detector-spectral-imagery-using-sparse-spatiospectral-masks
Abstract : Two model-based algorithms for edge detection in spectral imagery are developed that specifically target
capturing intrinsic features such as isoluminant edges that are characterized by a jump in color but not in intensity.
Given prior knowledge of the classes of reflectance or emittance spectra associated with candidate objects in a
scene, a small set of spectral-band ratios, which most profoundly identify the edge between each pair of materials,
are selected to define a edge signature. The bands that form the edge signature are fed into a spatial mask,
producing a sparse joint spatiospectral nonlinear operator. The first algorithm achieves edge detection for every
material pair by matching the response of the operator at every pixel with the edge signature for the pair of materials.
The second algorithm is a classifier-enhanced extension of the first algorithm that adaptively accentuates distinctive
features before applying the spatiospectral operator. Both algorithms are extensively verified using spectral imagery
from the airborne hyperspectral imager and from a dots-in-a-well midinfrared imager. In both cases, the multicolor
gradient (MCG) and the hyperspectral/spatial detection of edges (HySPADE) edge detectors are used as a
benchmark for comparison. The results demonstrate that the proposed algorithms outperform the MCG and
HySPADE edge detectors in accuracy, especially when isoluminant edges are present. By requiring only a few bands
as input to the spatiospectral operator, the algorithms enable significant levels of data compression in band selection.
In the presented examples, the required operations per pixel are reduced by a factor of 71 with respect to those
required by the MCG edge detector.
Title :Modeling of Speaking Rate Influences on Mandarin Speech Prosody and Its Application to Speaking
Rate-controlled TTS
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/application-speaking-rate-controlled-tts
Abstract : A new data-driven approach to building a speaking rate-dependent hierarchical prosodic model (SR-HPM),
directly from a large prosody-unlabeled speech database containing utterances of various speaking rates, to describe
the influences of speaking rate on Mandarin speech prosody is proposed. It is an extended version of the existing
HPM model which contains 12 sub-models to describe various relationships of prosodic-acoustic features of speech
signal, linguistic features of the associated text, and prosodic tags representing the prosodic structure of speech. Two
main modifications are suggested. One is designing proper normalization functions from the statistics of the whole
database to compensate the influences of speaking rate on all prosodic-acoustic features. Another is modifying the
HPM training to let its parameters be speaking-rate dependent. Experimental results on a large Mandarin read speech
corpus showed that the parameters of the SR-HPM together with these feature normalization functions interpreted the
effects of speaking rate onMandarin speech prosody very well. An application of the SR-HPM to design and
implement a speaking rate-controlledMandarin TTS system is demonstrated. The system can generate natural
synthetic speech for any given speaking rate in awide range of 3.4-6.8 syllables/sec.Two subjective tests,MOSand
preference test,were conducted to compare the proposed system with the popular HTS system. TheMOS scores of
the proposed system were in the range of 3.58-3.83 for eight different speaking rates, while they were in 3.09-3.43 for
HTS. Besides, the proposed system had higher preference scores (49.8%-79.6%) than those (9.8%-30.7%) ofHTS.
This confirmed the effectiveness of the speaking rate control method of the proposed TTS system.
Title :Multisensor Fusion-Based Concurrent Environment Mapping and Moving Object Detection for Intelligent
Service Robotics
Language : Matlab

Project Link : http://kasanpro.com/p/matlab/moving-object-detection-intelligent-service-robotics
Abstract : Intelligent service robot development is an important and critical issue for human community applications.
With the diverse and complex service needs, the perception and navigation are essential subjects. This investigation
focuses on the synergistic fusion of multiple sensors for an intelligent service robot that not only performs
self-localization and mapping but also detects moving objects or people in the building it services. First of all, a new
augmented approach of graph-based optimal estimation was derived for concurrent robot postures and moving object
trajectory estimate. Moreover, all the moving object detection issues of a robot's indoor navigation are divided and
conquered via multisensor fusion methodologies. From bottom to up, the estimation fusion methods are tactically
utilized to get a more precise result than the one from only the laser ranger or stereo vision. Furthermore, for solving
the consistent association problem of moving objects, a covariance area intersection belief assignment is applied for
motion state evaluation and the complementary evidences such as kinematics and vision features are both
synergized together to enhance the association efficiency with the evidence fusion method. The proof of concept with
experiments has been successfully demonstrated and analyzed.
Title :Personalized Geo-Specific Tag Recommendation for Photos on Social Websites
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/personalized-geo-specific-tag-recommendation-photos-social-websites
Abstract : Social tagging becomes increasingly important to organize and search large-scale community-contributed
photos on social websites. To facilitate generating high-quality social tags, tag recommendation by automatically
assigning relevant tags to photos draws particular research interest. In this paper, we focus on the personalized tag
recommendation task and try to identify user-preferred, geo-location-specific as well as semantically relevant tags for
a photo by leveraging rich contexts of the freely available community-contributed photos. For users and geo-locations,
we assume they have different preferred tags assigned to a photo, and propose a subspace learning method to
individually uncover the both types of preferences. The goal of our work is to learn a unified subspace shared by the
visual and textual domains to make visual features and textual information of photos comparable. Considering the
visual feature is a lower level representation on semantics than the textual information, we adopt a progressive
learning strategy by additionally introducing an intermediate subspace for the visual domain, and expect it to have
consistent local structure with the textual space. Accordingly, the unified subspace is mapped from the intermediate
subspace and the textual space respectively. We formulate the above learning problems into a united form, and
present an iterative optimization with its convergence proof. Given an untagged photo with its geo-location to a user,
the user-preferred and the geo-location-specific tags are found by the nearest neighbor search in the corresponding
unified spaces. Then we combine the obtained tags and the visual appearance of the photo to discover the
semantically and visually related photos, among which the most frequent tags are used as the recommended tags.
Experiments on a large-scale data set collected from Flickr verify the effectivity of the proposed solution.
Title :Photometric Stereo Using Sparse Bayesian Regression for General Diffuse Surfaces
Language : Matlab
Project Link :
http://kasanpro.com/p/matlab/photometric-stereo-using-sparse-bayesian-regression-general-diffuse-surfaces
Abstract : Most conventional algorithms for non-Lambertian photometric stereo can be partitioned into two
categories. The first category is built upon stable outlier rejection techniques while assuming a dense Lambertian
structure for the inliers, and thus performance degrades when general diffuse regions are present. The second utilizes
complex reflectance representations and non-linear optimization over pixels to handle non-Lambertian surfaces, but
does not explicitly account for shadows or other forms of corrupting outliers. In this paper, we present a purely
pixel-wise photometric stereo method that stably and efficiently handles various non-Lambertian effects by assuming
that appearances can be decomposed into a sparse, non-diffuse component (e.g., shadows, specularities, etc.) and a
diffuse component represented by a monotonic function of the surface normal and lighting dot-product. This function
is constructed using a piecewise linear approximation to the inverse diffuse model, leading to closed-form estimates
of the surface normals and model parameters in the absence of non-diffuse corruptions. The latter are modeled as
latent variables embedded within a hierarchical Bayesian model such that we may accurately compute the unknown
surface normals while simultaneously separating diffuse from non-diffuse components. Extensive evaluations are
performed that show state-of-the-art performance using both synthetic and real-world images.
Title :Quality Assessment of Stereoscopic 3D Image Compression by Binocular Integration Behaviors
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/quality-assessment-stereoscopic-3d-image-compression-binocular-integration-beh

Abstract : The objective approaches of 3D image quality assessment play a key role for the development of
compression standards and various 3D multimedia applications. The quality assessment of 3D images faces more
new challenges, such as asymmetric stereo compression, depth perception, and virtual view synthesis, than its 2D
counterparts. In addition, the widely used 2D image quality metrics (e.g., PSNR and SSIM) cannot be directly applied
to deal with these newly introduced challenges. This statement can be verified by the low correlation between the
computed objective measures and the subjectively measured mean opinion scores (MOSs), when 3D images are the
tested targets. In order to meet these newly introduced challenges, in this paper, besides traditional 2D image
metrics, the binocular integration behaviors--the binocular combination and the binocular frequency integration, are
utilized as the bases for measuring the quality of stereoscopic 3D images. The effectiveness of the proposed metrics
is verified by conducting subjective evaluations on publicly available stereoscopic image databases. Experimental
results show that significant consistency could be reached between the measured MOS and the proposed metrics, in
which the correlation coefficient between them can go up to 0.88. Furthermore, we found that the proposed metrics
can also address the quality assessment of the synthesized color-plusdepth 3D images well. Therefore, it is our belief
that the binocular integration behaviors are important factors in the development of objective quality assessment for
3D images.
Title :Robust Semi-Automatic Depth Map Generation in Unconstrained Images and Video Sequences for 2D to
Stereoscopic 3D Conversion
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/depth-map-generation-unconstrained-images
Abstract : We describe a system for robustly estimating synthetic depth maps in unconstrained images and videos,
for semi-automatic conversion into stereoscopic 3D. Currently, this process is automatic or done manually by
rotoscopers. Automatic is the least labor intensive, but makes user intervention or error correction difficult. Manual is
the most accurate, but time consuming and costly. Noting the merits of both, a semi-automatic method blends them
together, allowing for faster and accurate conversion. This requires user-defined strokes on the image, or over several
keyframes for video, corresponding to a rough estimate of the depths. After, the rest of the depths are determined,
creating depth maps to generate stereoscopic 3D content, with Depth Image Based Rendering to generate the
artificial views. Depth map estimation can be considered as a multi-label segmentation problem: each class is a
depth. For video, we allow the user to label only the first frame, and we propagate the strokes using computer vision
techniques. We combine the merits of two well-respected segmentation algorithms: Graph Cuts and Random Walks.
The diffusion from Random Walks, with the edge preserving of Graph Cuts should give good results. We generate
good quality content, more suitable for perception, compared to a similar framework.
Title :Sharing Visual Secrets in Single Image Random Dot Stereograms
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/sharing-visual-secrets-single-image-random-dot-stereograms
Abstract : Visual cryptography schemes (VCSs) generate random and meaningless shares to share and protect
secret images. Conventional VCSs suffer from a transmission risk problem because the noise-like shares will raise
the suspicion of attackers and the attackers might intercept the transmission. Previous research has involved in hiding
shared content in halftone shares to reduce these risks, but this method exacerbates the pixel expansion problem and
visual quality degradation problem for recovered images. In this paper, a binocular VCS (BVCS), called the (2,
n)-BVCS, and an encryption algorithm are proposed to hide the shared pixels in the single image random dot
stereograms (SIRDSs). Because the SIRDSs have the same 2D appearance as the conventional shares of a VCS,
this paper tries to use SIRDSs as cover images of the shares of VCSs to reduce the transmission risk of the shares.
The encryption algorithm alters the random dots in the SIRDSs according to the construction rule of the (2, n)-BVCS
to produce nonpixelexpansion shares of the BVCS. Altering the dots in a SIRDS will degrade the visual quality of the
reconstructed 3D objects. Hence, we propose an optimization model that is based on the visual quality requirement of
SIRDSs to develop construction rules for a (2, n)-BVCS that maximize the contrast of the recovered image in the
BVCS.
Title :Single-Image Superresolution of Natural Stochastic Textures Based on Fractional Brownian Motion
Language : Matlab
Project Link :
http://kasanpro.com/p/matlab/superresolution-natural-stochastic-textures-based-fractional-brownian-motion
Abstract : Texture enhancement presents an ongoing challenge, in spite of the considerable progress made in recent

years. Whereas most of the effort has been devoted so far to enhancement of regular textures, stochastic textures
that are encountered in most natural images, still pose an outstanding problem. The purpose of enhancement of
stochastic textures is to recover details, which were lost during the acquisition of the image. In this paper, a texture
model, based on fractional Brownian motion (fBm), is proposed. The model is global and does not entail using image
patches. The fBm is a self-similar stochastic process. Self-similarity is known to characterize a large class of natural
textures. The fBm-based model is evaluated and a single-image regularized superresolution algorithm is derived. The
proposed algorithm is useful for enhancement of a wide range of textures. Its performance is compared with
single-image superresolution methods and its advantages are highlighted.
Title :Weighted KPCA Degree of Homogeneity Amended Nonclassical Receptive Field Inhibition Model for Salient
Contour Extraction in Low-Light-Level Image
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/nonclassical-receptive-field-inhibition-model-salient-contour-extraction
Abstract : The stimulus response of the classical receptive field (CRF) of neuron in primary visual cortex is affected
by its periphery [i.e., non-CRF (nCRF)]. This modulation exerts inhibition, which depends primarily on the correlation
of both visual stimulations. The theory of periphery and center interaction with visual characteristics can be applied in
night vision information processing. In this paper, a weighted kernel principal component analysis (WKPCA) degree of
homogeneity (DH) amended inhibition model inspired by visual perceptual mechanisms is proposed to extract salient
contour from complex natural scene in low-light-level image. The core idea is that multifeature analysis can recognize
the homogeneity in modulation coverage effectively. Computationally, a novel WKPCA algorithm is presented to
eliminate outliers and anomalous distribution in CRF and accomplish principal component analysis precisely. On this
basis, a new concept and computational procedure for DH is defined to evaluate the dissimilarity between periphery
and center comprehensively. Through amending the inhibition from nCRF to CRF by DH, our model can reduce the
interference of noises, suppress details, and textures in homogeneous regions accurately. It helps to further avoid
mutual suppression among inhomogeneous regions and contour elements. This paper provides an improved
computational visual model with high-performance for contour detection from cluttered natural scene in night vision
image.
Title :Coupled Variational Image Decomposition and Restoration Model for Blurred Cartoon-Plus-Texture Images
With Missing Pixels
Language : Java
Project Link : http://kasanpro.com/p/java/cartoon-plus-texture-image-decomposition-restoration
Abstract : In this paper, we develop a decomposition model to restore blurred images with missing pixels. Our
assumption is that the underlying image is the superposition of cartoon and texture components. We use the total
variation norm and its dual norm to regularize the cartoon and texture, respectively. We recommend an efficient
numerical algorithm based on the splitting versions of augmented Lagrangian method to solve the problem.
Theoretically, the existence of a minimizer to the energy function and the convergence of the algorithm are
guaranteed. In contrast to recently developed methods for deblurring images, the proposed algorithm not only gives
the restored image, but also gives a decomposition of cartoon and texture parts. These two parts can be further used
in segmentation and inpainting problems. Numerical comparisons between this algorithm and some state-of-the-art
methods are also reported.
Title :Region-Based Iterative Reconstruction of Structurally Changing Objects in CT
Language : Java
Project Link : http://kasanpro.com/p/java/region-based-iterative-reconstruction-structurally-changing-objects-ct
Abstract : X-ray computed tomography (CT) is a powerful tool for noninvasive imaging of time-varying objects. In the
past, methods have been proposed to reconstruct images from continuously changing objects. For discretely or
structurally changing objects, however, such methods fail to reconstruct high quality images, mainly because
assumptions about continuity are no longer valid. In this paper, we propose a method to reconstruct structurally
changing objects. Starting from the observation that there exist regions within the scanned object that remain
unchanged over time, we introduce an iterative optimization routine that can automatically determine these regions
and incorporate this knowledge in an algebraic reconstruction method. The proposed algorithm was validated on
simulation data and experimental ?CT data, illustrating its capability to reconstruct structurally changing objects more
accurately in comparison to current techniques.
Title :An Automatic Graph-Based Approach for Artery/Vein Classification in Retinal Images

Language : Java
Project Link : http://kasanpro.com/p/java/an-automatic-graph-based-artery-vein-classification-retinal-images
Abstract : The classification of retinal vessels into artery/vein (A/V) is an important phase for automating the
detection of vascular changes, and for the calculation of characteristic signs associated with several systemic
diseases such as diabetes, hypertension, and other cardiovascular conditions. This paper presents an automatic
approach for A/V classification based on the analysis of a graph extracted from the retinal vasculature. The proposed
method classifies the entire vascular tree deciding on the type of each intersection point (graph nodes) and assigning
one of two labels to each vessel segment (graph links). Final classification of a vessel segment as A/V is performed
through the combination of the graph-based labeling results with a set of intensity features. The results of this
proposed method are compared with manual labeling for three public databases. Accuracy values of 88.3%, 87.4%,
and 89.8% are obtained for the images of the INSPIREAVR, DRIVE, and VICAVR databases, respectively. These
results demonstrate that our method outperforms recent approaches for A/V classification.

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