Image segmentation
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The document discusses implementing the watershed algorithm for image segmentation using MATLAB. It begins with an overview of the watershed algorithm which uses region growing to segment images based on gradients. It then discusses image segmentation goals and different categories of segmentation algorithms including discontinuity, similarity, thresholding and region-based approaches. The document outlines steps to be taken which include preprocessing images, implementing the watershed algorithm, and analyzing results to see the difference in segmented images. It expects watershed processing to produce segmented regions from a test image.
Image segmentation
- 1. Study and Implementation of Watershed Algorithm using MATLAB Supervisor– Prof. Sanjeev Kumar By– Mukul Jindal 1
- 2. Watershed Algorithm The watershed transformation is a technique for segmenting digital images that uses a type of region growing method based on an image gradient. It thus effectively combines elements from both the discontinuity and similarity methods described below. 2
- 3. What is Image Segmentation The goal of image segmentation is to reduce the number of colours in the input reference image and then group neighbouring pixels of similar colour together to form bounded segments Segmentation subdivides an image into its constituent regions or groups. The level to which the subdivision is carried depends on the problem being solved. That is, segmentation should stop when the objects of interest in an application have been isolated. e.g. automated inspection of electronic assemblies; specific anomalies; missing components or broken connection paths. 3
- 4. Image Segmentation algorithm It is based on two basic properties of intensity values : discontinuity and similarity First Category : Abrupt changes in intensity. Second Category : Partitioning of regions which are similar according to a set of predefined criteria. e.g. thresholding, region growing, region splitting and merging. 4
- 5. First Category is further subdivided into- •Points •Lines •Edges 5
- 6. Detection of discontinuities Points, lines, edges The most common way R = w1*z1 + w2*z2 + ……+ w9*z9 6
- 7. Point detection R T T = Threshold 7
- 8. Point detection (b) X-ray image (c) Result of (d) Result of point of a turbine blade point detection detection mask with porosity mask with threshold 8
- 9. Line detection – A Suitable Mask in desired direction – Thresholding 9
- 10. Line detection • Example: -45º Mask Thresholding 10
- 11. Edge Detection – Two Mathematical model 11
- 12. Edge Detection Gray level profile First derivative Second derivative 12
- 13. Gradient Operators Y-direction X-direction 13
- 14. Diagonal Edge 45-Direction -45-Direction 14
- 15. Diagonal edge detection 15
- 16. Things done so far • Read about different Image Segmentation processes. • Working my way towards implementing Watershed algorithm using MATLAB. 16
- 17. Things to be done • Use preprocessing method to be implemented on images. • Implement Watershed Algorithm • Analyse and record the difference after processing. 17
- 18. Test Result Expected from Watershed Algorithm Test image After Watershed Algorithm 18
- 19. References - • Paul R. Hill. Wavelet Based Texture Analysis and Segmentation for Image Retrieval and Fusion. PhD thesis, University of Bristol, March 2002. • Richard E. Woods and R.C. Gonzalez. Digital Image Processing. Pearson Education, 2005. 19
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