IRJET- Multi Image Morphing: A Review

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 05 Issue: 11 | Nov 2018 www.irjet.net p-ISSN: 2395-0072
© 2018, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 683
Multi Image Morphing: A Review
Mahip Bartere1, Bhagyashri Kandalkar 2, H R Deshmukh3
1Mahip M Bartere, Professor, Dept. of ME CSE, GHRCEMA, Maharashtra, India
3H R Deshmukh, Professor, Dept. of ME CSE, GHRCEMA
2Bhagyashri Kandalkar, ME CSE, GHRCEMA
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Abstract - Low contrast images can result from the wrong
setting of image achievement or poor illumination situation.
Such images may not be visually attractive andcanbedifficult
for feature extraction. Contrast enhancement of color images
can be useful in medical area for image inspection. In this
paper, a new technique is proposed to develop the contrast of
color images. The RGB (red, green, blue)colorimage isunclear
into normalized RGB color space. Adaptive histogram
equalization method is applied to each ofthethreechannelsof
normalized RGB color space. The equivalent channels in the
original image (low contrast) and that of contrast enhanced
image with adaptive histogram equalization (AHE) are
morphed together in proper proportions. Theproposedsystem
is tested on seventy color imagesofacnepatients. Theoutcome
of the proposed technique are analyzed using combined
variance and contrast improvement cause measures. The
results are also compared with decorrelation stretch. Both
subjective and quantitative analysis demonstrates that the
proposed techniques better the other techniques.
Key Words: (Normalized RGB, adaptive histogram
equalization, cumulative variance.)
1. INTRODUCTION
CONTRAST improvement is an important step in
computer vision and image processing. Low contrast
images may result from poor explanation condition
during image acquisition or other aberrations of the
image capturing and display devices [1]. In low
contrast images, the image relevant details are not
vivid. Especially in a medical area where images of the
patients of different diseases are examined, and
severity of the disease is evaluated.
The histogram is used as a fundament tool for findingthe
distribution of gray-level values in an image. The
histogram of low contrast image spans over a small
portion of the range of intensity values [2]. The
histogram of well-contrasted image covers the whole
dynamic range of intensity. Well-contrasted images are
visually appealing and prove effectual in subjective
evaluation. Along with specular illumination removal,
contrast enhancement is also used as an necessary step
in medical image processing [3]-[5].
The difference enhancement technique can be separated
into two main types; direct enhancement and indirect
enhancement techniques. By direct techniques, the
intensity values of pixels in an image are modified by
directly processing [6]-[8]. While indirecttechniquesare
based on re-distribution of gray level values in an image
by calculating collective distribution function (CDF) [9].
With the help of CDF, the histogram of graylevel valuesis
extended to the full dynamic range. The difference in
monochrome images is measured by variance or
standard deviation of gray level values. These variations
in gray level values are attributedtothespatial frequency
to which human visual system is very sensitive. The
spatial frequency relevant information is captured by
edges and usually calculated with the first derivative.
Difference enhancement of color images is very difficult.
If the ratio of the components of RGB color space is
changed, the color of the new image may look completely
different than the original one.Thetechniquesdeveloped
for gray images can be applied to color images in two
ways. In the first approach, each channel of RGB color
space is treated as a gray image. The method isappliedto
each of the three channels separately and combined
together after processing. In the second approach, the
RGB color image is first converted into a color space in
which the chrominance and luminance components are
separate from each other. The contrast enhancement
technique is applied to the luminance component and
leaving the chrominance part untouched. It is claimed
that the hue is preserved while the contrast of the image
is enhanced [10].
2. LITERATURE REVIEW
2.1 Cross Dissolve Morphing
Before the enhancement of morphing, the image
transitions were commonly achieved throughout the use of
cross-dissolves, e.g., linear intermission to fade from one
image to another. Figure. 1 depicts this process applied over
five frames. The result is poor, remaining to the double-
exposure effect visible in misaligned regions. This problem is
mostly apparent in the middle frame,wherebothinputimages
contribute equally to the output. Morphing achieves a liquid
transformation by incorporating warping to maintain
arithmetic alignment throughout the cross-dissolve process.

Fig-1: Example of cross-dissolve morphing
2.2 Mesh Warping
Mesh warping was pioneered at Industrial Light &
Magic (ILM) by Douglas Smythe for use in the movieWillow
in 1988. It has been successfully used in many subsequent
motion pictures [1]. To demonstrate the 2-pass mesh
warping algorithm, consider the image sequence shown in
Fig.-2. The five frames in the middle row represent a
metamorphosis (or morph) between the two faces at both
ends of the row [1]. We will refer to these two images as IS
and IT, the source and the target images, respectively. ‘Five
source images has mesh MS associated with it that specifies
the coordinates of control points, or landmarks. A next
mesh, MT, specifies their corresponding positions in the
target image. MeshesMS and MT are respectively shown
overlaid on Island IT in the upper left and lower right
images of the figure. Notice that landmarkssuchastheeyes,
nose, and lips lie below corresponding grid lines in both
meshes. Together, MS and MT are used to define the spatial
transformation that maps all points in IS onto IT. The
meshes are constrained to be topologically equivalent, i.e.,
no folding or discontinuities are permitted. Therefore, the
nodes in MT may wander as far from MS as necessary, as
long as they do not cause self-intersection.Furthermore,for
simplicity, the meshes are constrained to have frozen
borders. All transitional frames in the morph sequence are
the product of a 4-step process:
2.3 Field Morphing
While meshes show to be a suitable manner of
specifying pairs of feature points, they are, however,
sometimes cumbersome to use [1]. The field morphing
algorithm developed by Beier and Neelyat Pacific Data
Images grew out of the desire to simplify the user
interface to handle correspondence by means of line
pairs. A pair of corresponding lines in the source and
target images defines a coordinate mapping betweenthe
two images. In addition to the straightforward
correspondence provided for all points along the lines,
the mapping of points in the vicinity of the line can be
determined by their distance fromtheline. Sincemultiple
line pairs are usually given, the displacementofa pointin
the source image is actually a weighted sum of the
mappings due to each line pair, with the weights
attributed to distance and line length. This approach has
the benefit of being more expressive than mesh warping.
2.4 Point Distribution Method
This method uses points that the users fix to each
main feature of the face, to help map the source image
and the destination image together. The computation
uses these points to calculate the result images. The
resulting images from this work are satisfactory to
theusers. However, it is not automatic, because the users
have to fix the mapping point of the features before
making the program merge them together. [11, 6]
2.5 Critical Point Filters Method
The critical point filters techniquecanextractthemain
features of the face by using the color differentiation in
the features. The maximumsub-imagecanextracttheeye
and hair of the face, the max-min saddle sub image can
extract lips, the min-max saddle sub-image can extract
the skin and the minimum sub-image can extract the
background of the image [6, 12, 13].
3. CONCLUSION
Morphing algorithms commonly share some mechanism
such as feature specification, warpGenerationandtransition
control. The ease with which researchers can efficiently use
morphing tools is determined by the manner in which these
components are addressed. We brieflysurveyedwidelyused
morphing techniques such as mesh warping and field
Morphing.
REFERENCES
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