Segmentation and Region Detection

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 ConnectivityProblem

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