Image Analysis: Object Recognition

1. Image Analysis: Object Recognition

2. Image Segmentation Image Analysis: Object Recognition INPUT IMAGE OBJECT IMAGE Image Segmentation: each object in the image is identified and isolated from the rest of the image

3. Feature Extraction Image Analysis: Object Recognition x x x … x OBJECT IMAGE 1 2 3 n FEATURE VECTORS Feature Extraction: measurements or “features” are computed on each object identified during the segmentation step

4. x x x 1 2 n The feature vector for a given pixel consists of the corresponding pixels from each feature image; the feature vector for an object would be computed from pixels comprising the object, from each feature image.

5. Classification Image Analysis: Object Recognition FEATURE VECTORS OBJECT TYPE “WRENCH” Classification: each object is assigned to a class

6. Image Segmentation Feature Extraction Classification Image Analysis: Object Recognition INPUT IMAGE OBJECT IMAGE FEATURE VECTOR OBJECT TYPE “WRENCH”

7. Example: an automated fruit sorting system

8. Example: an automated fruit sorting system segmentation: identify the fruit objects the image is partitioned to isolate individual fruit objects

9. Example: an automated fruit sorting system segmentation: identify the fruit objects feature extraction: compute a size and color feature for each segmented region in the image size - diameter of each object color - red-to-green brightness ratio (redness measure)

10. Example: an automated fruit sorting system segmentation: identify the fruit objects feature extraction: compute a size and color feature for each segmented region in the image classification: partition the “fruit” objects in feature space

12. Automatic (unsupervised) image Segementation : difficult problem 1) attempt to control imaging conditions (industrial applications) 2) choose sensor which enhance objects of interest (infared imaging)

13. Segmentation Algorithms: - discontinuities between homogeneous regions - similarity of pixel values within a region

14. Discontinuity based Segmentation: detect points, lines and edges in an image

15. Discontinuity based Segmentation: detect points, lines and edges in an image -1 -1 -1 -1 8 -1 -1 -1 -1

16. Discontinuity based Segmentation: detect points, lines and edges in an image -1 -1 -1 -1 8 -1 -1 -1 -1 -1 -1 -1 2 2 2 -1 -1 -1 -1 2 -1 -1 2 -1 -1 2 -1 -1 -1 2 -1 2 -1 2 -1 -1 2 -1 -1 -1 2 -1 -1 -1 2

17. Discontinuity based Segmentation: detect points, lines and edges in an image -1 -1 -1 -1 8 -1 -1 -1 -1 -1 -1 -1 2 2 2 -1 -1 -1 -1 2 -1 -1 2 -1 -1 2 -1 -1 0 1 -2 0 2 -1 0 1 -1 -2 -1 0 0 0 1 2 1 -1 -1 2 -1 2 -1 2 -1 -1 2 -1 -1 -1 2 -1 -1 -1 2

18. Discontinuity based Segmentation: detect points, lines and edges in an image -1 -2 -1 0 0 0 1 2 1 -1 0 1 -2 0 2 -1 0 1 Gx Gy

19. Discontinuity based Segmentation: detect points, lines and edges in an image -1 -2 -1 0 0 0 1 2 1 -1 0 1 -2 0 2 -1 0 1 Gx Gy

20. Discontinuity based Segmentation: Gx Gradient vector Gy Edge Linking - used to create connected boundaries

21. Discontinuity based Segmentation: Gx Gradient vector Gy Edge Linking - used to create connected boundaries - similar points within a neighborhood are linked

22. Discontinuity based Segmentation: Gx Gradient vector Gy Edge Linking - used to create connected boundaries - similar points within a neighborhood are linked magnitude of gradient vector [ Gx + Gy ] 1 2 2 2

23. Discontinuity based Segmentation: Gx Gradient vector Gy Edge Linking - used to create connected boundaries - similar points within a neighborhood are linked magnitude of gradient vector [ Gx + Gy ] approximated as | Gx | + | Gy | 1 2 2 2

24. Discontinuity based Segmentation: Gx Gradient vector Gy Edge Linking - used to create connected boundaries - similar points within a neighborhood are linked magnitude of gradient vector orientation of edges ang(x,y) = tan ( ) -1 Gy Gx

25. Discontinuity based Segmentation: Gx Gradient vector Gy Edge Linking - used to create connected boundaries - similar points within a neighborhood are linked magnitude of gradient vector orientation of edges

26. Discontinuity based Segmentation: Identify zero crossings

27. Discontinuity based Segmentation: Identify zero crossings 0 -1 0 -1 4 -1 0 -1 0

28. Discontinuity based Segmentation: Identify zero crossings

29. Discontinuity based Segmentation: Identify zero crossings

30. Discontinuity based Segmentation: Identify zero crossings

31. Discontinuity based Segmentation: Identify zero crossings

32. Discontinuity based Segmentation: Identify zero crossings

33. Similarity based Segmentation: - Simple thresholding - Split and Merge - Recursive thresholding

34. Similarity based Segmentation: - Simple thresholding - Split and Merge - Recursive thresholding

35. Single Level Thresholding 0, g < TH G - 1, TH # g T[g] =

36. Single Level Thresholding 0, g < TH G - 1, TH # g T[g] =

37. Single Level Thresholding

38. Single Level Thresholding 0, g < TH G - 1, TH # g T[g] =

39. Multiple Level Thresholding 0, g < TH1 G - 1, TH1# g <= TH2 0, g > TH2 T[g] =

40. Similarity based Segmentation: - Simple thresholding - Split and Merge - Recursive thresholding

41. U Split and Merge 1) split region into four disjoint quadrants if P(Rj) = FALSE 2) merge any adjacent regions Rj and Rk if P(Rj Rk) = TRUE 3) stop when no splitting or merging is possible

42. Split and Merge

43. Split and Merge

44. Split and Merge

45. Split and Merge

46. Split and Merge

47. Split and Merge

48. Split and Merge

49. Split and Merge