Segmentation Active Contours Split and Merge Watershed Region Splitting and Merging Graph-based Segmentation Mean shift and Model finding Normalized Cut

1. Computer Vision
Chap.4 : Segmentation
SUB CODE: 3171614
SEMESTER: 7TH IT
PREPARED BY:
PROF. KHUSHALI B. KATHIRIYA

2. Outline
• Active Contours
• Split and Merge
• Watershed
• Region Splitting and Merging
• Graph-based Segmentation
• Mean shift and Model finding
• Normalized Cut
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3. Active Contours
PREPARED BY:
PROF. KHUSHALI B. KATHIRIYA

4. Active Contour (Boundary Detection)
• Segmentation is a section of image processing for the separation or segregation
of information from the required target region of the image. There are different
techniques used for segmentation of pixels of interest from the image.
• Active contour is one of the active models in segmentation techniques, which
makes use of the energy constraints and forces in the image for separation of
region of interest. Active contour defines a separate boundary or curvature for
the regions of target object for segmentation.
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5. Active Contour(Cont.)
• Application of Active Contour
• Medical Imaging
• Brain CT images
• MRI images
• Cardiac images
• Motion Tracking
• Stereo Tracking
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6. What is Active Contour?
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7. What is Active Contour?
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8. Example of Active Contour
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9. Example of Active Contour (Cont.)
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Medical Imaging

10. Example of Active Contour (Cont.)
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Motion Tracking

11. Split and Merge
PREPARED BY:
PROF. KHUSHALI B. KATHIRIYA

12. Split and Merge
• Split and merge segmentation is an image processing technique used
to segment an image. The image is successively split into quadrants based on
a homogeneity criterion and similar regions are merged to create the segmented
result. The technique incorporates a quadtree data structure, meaning that there
is a parent-child node relationship. The total region is a parent, and each of the
four splits is a child.
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13. Split and Merge
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14. Split and Merge Example
• The following example shows the segmentation of a gray scale image using
matlab. The homogeneity criterion is thresholding, max(region)-min(region) < 10
for a region to be homogeneous.
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15. Split and Merge Example
• The blocks created during splitting are shown in the following picture:
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16. Split and Merge Example
• And the segmented image is below.
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17. Region Split and Merge
PREPARED BY:
PROF. KHUSHALI B. KATHIRIYA

18. Region Split and Merge Example (Cont.)
• Apply Region Split on following image. Assume that threshold value be <=3.
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5 6 6 6 7 7 6 6
6 7 6 7 5 5 4 7
6 6 4 4 3 2 5 6
5 4 5 4 2 3 4 6
0 3 2 3 3 2 4 7
0 0 0 0 2 2 5 6
1 1 0 1 0 3 4 4
1 0 1 0 2 3 5 4

19. Region Split and Merge Example (Cont.)
• Step 1: Identify Max and Min pixel value from the whole image
• Max = 7
• Min = 0
• Max-Min= 7
• 7 <= 3 (Condition false)
• Split image
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5 6 6 6 7 7 6 6
6 7 6 7 5 5 4 7
6 6 4 4 3 2 5 6
5 4 5 4 2 3 4 6
0 3 2 3 3 2 4 7
0 0 0 0 2 2 5 6
1 1 0 1 0 3 4 4
1 0 1 0 2 3 5 4

20. Region Split and Merge Example (Cont.)
• Step 1: Identify Max and Min pixel value from the whole image
• Max = 7
• Min = 0
• Max-Min= 7
• 7 <= 3 (Condition false)
• Split R image into R1a,R2b,cR3,R4d
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5 6 6 6 7 7 6 6
6 7 6 7 5 5 4 7
6 6 4 4 3 2 5 6
5 4 5 4 2 3 4 6
0 3 2 3 3 2 4 7
0 0 0 0 2 2 5 6
1 1 0 1 0 3 4 4
1 0 1 0 2 3 5 4
R1a R2b
R3c R4d

21. Region Split and Merge Example (Cont.)
• Step 2: Compute for R1a region. Min and Max
• Max=7
• Min=4
• Max-Min=7-4=3
• 3 <= 3 (Condition true)
• No need to split R1a
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5 6 6 6 7 7 6 6
6 7 6 7 5 5 4 7
6 6 4 4 3 2 5 6
5 4 5 4 2 3 4 6
0 3 2 3 3 2 4 7
0 0 0 0 2 2 5 6
1 1 0 1 0 3 4 4
1 0 1 0 2 3 5 4
R1a R2b
R3c R4d

22. Region Split and Merge Example (Cont.)
• Step 3: compute for R3c region. Min and Max
• Max=3
• Min=0
• Max-Min=3-0=3
• 3 <= 3 (Condition true)
• No need to split R3c
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5 6 6 6 7 7 6 6
6 7 6 7 5 5 4 7
6 6 4 4 3 2 5 6
5 4 5 4 2 3 4 6
0 3 2 3 3 2 4 7
0 0 0 0 2 2 5 6
1 1 0 1 0 3 4 4
1 0 1 0 2 3 5 4
R1a R2b
R3c R4d

23. Region Split and Merge Example (Cont.)
• Step 4: Compute for R2b region. Min and Max
• Max=7
• Min=2
• Max-Min=7-2=5
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5 6 6 6 7 7 6 6
6 7 6 7 5 5 4 7
6 6 4 4 3 2 5 6
5 4 5 4 2 3 4 6
0 3 2 3 3 2 4 7
0 0 0 0 2 2 5 6
1 1 0 1 0 3 4 4
1 0 1 0 2 3 5 4
R1a R2b
R3c R4d

24. Region Split and Merge Example (Cont.)
• Step 4: Compute for R2b region. Min and Max
• Max=7
• Min=2
• Max-Min=7-2=5
• 5 <= 3 (Condition false)
• Split R2b into R2b1,R2b2,R2b3,R2b4.
Prepared by: Prof. Khushali B Kathiriya
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5 6 6 6 7 7 6 6
6 7 6 7 5 5 4 7
6 6 4 4 3 2 5 6
5 4 5 4 2 3 4 6
0 3 2 3 3 2 4 7
0 0 0 0 2 2 5 6
1 1 0 1 0 3 4 4
1 0 1 0 2 3 5 4
R1a R2b
R3c R4d

25. Region Split and Merge Example (Cont.)
• Step 5: Compute for R2b1,R2b2,R2b3,R2b4 region. Min and Max
• For all R2b1,R2b2,R2b3,R2b4
Condition have satisfied so no need
to splitting.
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5 6 6 6 7 7 6 6
6 7 6 7 5 5 4 7
6 6 4 4 3 2 5 6
5 4 5 4 2 3 4 6
0 3 2 3 3 2 4 7
0 0 0 0 2 2 5 6
1 1 0 1 0 3 4 4
1 0 1 0 2 3 5 4
R1a R2b
R3c R4d

26. Region Split and Merge Example (Cont.)
• Step 6: Compute for R4d region. Min and Max
• Max=7
• Min=0
• Max-Min=7-0=7
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5 6 6 6 7 7 6 6
6 7 6 7 5 5 4 7
6 6 4 4 3 2 5 6
5 4 5 4 2 3 4 6
0 3 2 3 3 2 4 7
0 0 0 0 2 2 5 6
1 1 0 1 0 3 4 4
1 0 1 0 2 3 5 4
R1a R2b
R3c R4d

27. Region Split and Merge Example (Cont.)
• Step 6: Compute for R4d region. Min and Max
• Max=7
• Min=0
• Max-Min=7-0=7
• 7 <= 3 (Condition false)
• Split R4d into R4d1,R4d2,R4d3,R4d4.
Prepared by: Prof. Khushali B Kathiriya
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5 6 6 6 7 7 6 6
6 7 6 7 5 5 4 7
6 6 4 4 3 2 5 6
5 4 5 4 2 3 4 6
0 3 2 3 3 2 4 7
0 0 0 0 2 2 5 6
1 1 0 1 0 3 4 4
1 0 1 0 2 3 5 4
R1a R2b
R3c R4d

28. Region Split and Merge Example (Cont.)
• Step 7: Compute for R4d1,R4d2,R4d3,R4d4 region. Min and Max
• For all R4d1,R4d2,R4d3,R4d4
Condition have satisfied so no need
to splitting.
Prepared by: Prof. Khushali B Kathiriya
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5 6 6 6 7 7 6 6
6 7 6 7 5 5 4 7
6 6 4 4 3 2 5 6
5 4 5 4 2 3 4 6
0 3 2 3 3 2 4 7
0 0 0 0 2 2 5 6
1 1 0 1 0 3 4 4
1 0 1 0 2 3 5 4
R1a R2b
R3c R4d

29. Watershed Segmentation
PREPARED BY:
PROF. KHUSHALI B. KATHIRIYA

30. Watershed Segmentation
• The watershed algorithm is a classic algorithm used for segmentation and is
especially useful when extracting touching or overlapping objects in images, such
as the coins in the figure above.
• Using traditional image processing methods such as thresholding and contour
detection, we would be unable to extract each individual coin from the image —
but by leveraging the watershed algorithm, we are able to detect and extract
each coin without a problem.
• When utilizing the watershed algorithm we must start with user-defined markers.
These markers can be either manually defined via point-and-click, or we
can automatically or heuristically define them using methods such as thresholding
and/or morphological operations.
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31. Watershed Segmentation (Cont.)
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32. Watershed Segmentation (Cont.)
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33. The problem with basic thresholding and contour
extraction
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34. Using the watershed algorithm for segmentation
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35. Graph based Segmentation
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PROF. KHUSHALI B. KATHIRIYA

36. Graph based Segmentation
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37. Measuring Affinity
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38. Graph Cut Segmentation
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39. Graph Cut Segmentation (Cont.)
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40. Graph Cut Segmentation (Cont.)
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41. Problem with Min-Cut
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42. Measure of Subgraph Size
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43. Normalized Cut (Ncut)
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PROF. KHUSHALI B. KATHIRIYA

44. Normalized Cut(Ncut)
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45. Ncut Segmentation Results
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46. The Concept of Mean Shift
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PROF. KHUSHALI B. KATHIRIYA

47. The Concept of Mean Shift
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48. The Concept of Mean Shift (Cont.)
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49. Hill Climbing using Mean Shift
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50. Hill Climbing using Mean Shift (Cont.)
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51. Hill Climbing using Mean Shift (Cont.)
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52. Hill Climbing using Mean Shift (Cont.)
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53. Hill Climbing using Mean Shift (Cont.)
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54. Hill Climbing using Mean Shift (Cont.)
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55. Mean Shift Algorithm
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56. K-mean Vs. Mean shift
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57. Example of K-mean Vs. Mean shift
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58. Result of Mean Shift Segmentation
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