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Image Analysis: Object Recognition

Image Analysis: Object Recognition. 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. Feature Extraction. Image Analysis: Object Recognition. x

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Image Analysis: Object Recognition

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  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

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

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

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

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

  15. 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

  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 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

  17. 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

  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: Gx Gradient vector Gy Edge Linking - used to create connected boundaries

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

  21. 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

  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 ] approximated as | 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 orientation of edges ang(x,y) = tan ( ) -1 Gy Gx

  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

  25. Discontinuity based Segmentation: Identify zero crossings

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

  27. Discontinuity based Segmentation: Identify zero crossings

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

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

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

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

  36. Single Level Thresholding

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

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

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

  40. 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

  41. Split and Merge

  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

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