2. Region Detection Defining regions of an image

4. Introduction All pixels belong to a region Object Part of object Background Find region Constituent pixels Boundary

5. Region Detection A set of pixels P An homogeneity predicate H(P) Partition P into regions {R}, such that

6. Point based methods – thresholding If Regions are different brightness or colour Then Can be differentiated using this

7. Global thresholds Compute threshold from whole image Incorrect in some regions

8. Local thresholds Divide image into regions Compute threshold per region Merge thresholds across region boundaries

9. Region Growing All pixels belong to a region Select a pixel Grow the surrounding region

10. Slow Algorithm If a pixel is Not assigned to a region Adjacent to region Has colour properties not different to region’s Then Add to region Update region properties

11. Split and Merge Initialise image as a region While region is not homogeneous Split into quadrants and examine homogeneity

12. Recursive Splitting Split(P) { If (!H(P)) { P  subregions 1 … 4; Split (subregion 1); Split (subregion 2); Split (subregion 3); Split (subregion 4); }

13. Recursive Merging If adjacent regions are Weakly split Weak edge Similar Similar greyscale/colour properties Merge them

14. Edge Following Detection Finds candidate edge pixels Following Links candidates to form boundaries

15. 4/8 Connectivity Problem

16. Contour Tracking Scan image to find first edge point Track along edge points Spurs? Endpoints? Join edge segments

17. Edge Linking Aggregate collinear chain codes Colinear? Sequential least squares tolerance band

18. Sequential Least Squares Accumulate best fitting line to segments and error When error exceeds a threshold, finish segment Tolerance Band Accumulate best fitting line to segments If new point lies more than  from line, finish segment

19. Hop Along Algorithm

20. Examples An example would show an edge detected image There would be a record of the edge points constituting each edge segment

21. Scale Based Methods Structures observed depend on scale of observation

22. Analysis Processing of an image should be at a level of detail appropriate to structures being sought Image pyramid Wavelet transform

23. Image Pyramid Reducing resolution Pixels in each layer computed by averaging groups of pixels in layer below. Or Use scale dependent operators – e.g. Marr Hildreth.

24. Wavelet Transform Transform image data Select coefficients Reverse transform

25. Watersheds of Gradient Magnitude Compare geographical watersheds Divide landscape into catchment basins Edges correspond to watersheds

26. Algorithm Locate local minima Flood image from these points When two floods meet Identify a watershed pixel Build a dam Continue flooding

27. Example watersheds local minima

29. watershed point

30. dam

32. Representing Regions Constituent pixels Boundary pixels

33. Region map As an array of region labels Pixel value = region label

37. Summary Region detection Growing Edge following Watersheds

38. I think there is a world market for maybe five computers Thomas J Watson, chairman IBM, 1943