1. Segmentation and Region Detection Defining regions in an image

2. Introduction All pixels belong to a region –object –part of object –background Find region –constituent pixels –boundary

3. Image Segmentation To distinguish objects from background To divide the image into regions or segments, each of which is in some sense homogeneous, but the union of adjacent segments is not homogeneous in the same sense. Homogeneity here is characterized by some properties like –smoothly varying intensity, similar statistics, or colour.

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

5. Image Segmentation Many techniques including –threshold techniques –edge-based methods –region-based techniques –Image primitive based –connectivity-preserving relaxation methods.

6. Threshold techniques make decisions based on local pixel information –are effective when the intensity levels of the objects fall squarely outside the range of levels in the background.

7. Point based methods – thresholding If –regions are different brightness or colour Then –can be differentiated using this

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

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

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

11. 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

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

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

14. Recursive Merging If adjacent regions are –weakly split weak edge –similar similar greyscale/colour properties Merge them

15. Edge Following Detection –finds candidate edge pixels Following –links candidates to form boundaries

16. 4/8 Connectivity Problem

17. Contour Tracking Scan image to find first edge point Track along edge points –spurs? –endpoints? Join edge segments There would be a record of the edge points constituting each edge segment

19. Representing Regions Constituent pixels Boundary pixels

20. Based on both regions and edges

21. Based on the combination of colour and texture

22. Active Contour Model- Snake A connectivity-preserving relaxation-based segmentation method, - active contour model – snake –The main idea is to start with some initial boundary shape represented in the form of spline curves, and iteratively modify it by applying various shrink/expansion operations according to some energy function. Concepts involved –Image gradient –Smooth operation –Histogram equalization –Energy functions

23. Snakes, Active/Dynamic Contours Borders follow outline of object Outline obscured? Snake provides a solution

24. Algorithm Snake computes smooth, continuous border Minimises –length of border –curvature of border Against an image property –gradient?

25. Minimisation Initialise snake Integrate energy along it Iteratively move snake to global energy minimum

27. Summary Image segmentation Region detection –growing –edge following