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Segmentation and Perceptual Grouping

Segmentation and Perceptual Grouping. (Introduction to Computer Vision, 11.1.04). Kaniza. The image of this cube contradicts the optical image. Perceptual Organization. Atomism, reductionism: Perception is a process of decomposing an image into its parts.

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Segmentation and Perceptual Grouping

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  1. Segmentation and Perceptual Grouping (Introduction to Computer Vision, 11.1.04) Kaniza

  2. The image of this cube contradictsthe optical image

  3. Perceptual Organization • Atomism, reductionism: • Perception is a process of decomposing an image into its parts. • The whole is equal to the sum of its parts. • Gestalt (Wertheimer, Köhler, Koffka 1912) • The whole is larger than the sum of its parts.

  4. Gestalt: apparent motion

  5. Gestalt: apparent motion

  6. Proximity Gestalt Principles

  7. Proximity Gestalt Principles • Proximity • Similarity

  8. Proximity Similarity Gestalt Principles • Proximity • Similarity • Continuity

  9. Closure Proximity Similarity Continuity Gestalt Principles

  10. Proximity Similarity Continuity Closure Gestalt Principles • Closure • Common Fate

  11. Proximity Similarity Continuity Gestalt Principles • Closure • Common Fate • Simplicity • Closure • Common Fate

  12. Mona Lisa

  13. Mona Lisa

  14. Smooth Completion • Isotropic • Smoothness • Minimal curvature • Extensibility Elastica:

  15. Elastica • Scale invariant (Weiss, Bruckstein & Netravali) • Approximation (Sharon, Brandt & Basri)

  16. (Sharon, Brandt & Basri)

  17. Hough Transform

  18. Hough Transform

  19. Curve Salience

  20. Saliency Network (Shashua & Ullman) Encourage • Length • Low curvature • Closure

  21. Saliency Network (Shashua & Ullman)

  22. Tensor Voting (Guy & Medioni) • Every edge element votes to all its circular edge completions • Vote attenuates with distance: e-αd • Vote attenuates with curvature: e-βk • Determine salience at every point using principal moments

  23. Tensor Voting (Guy & Medioni)

  24. Stochastic Completion Field (Mumford; Williams & Jacobs) • Random walk: • In addition, a particle may die with probability:

  25. Stochastic Completion Fields (Mumford; Williams & Jacobs) • Most probable path: with

  26. Stochastic Completion Fields (Mumford; Williams & Jacobs)

  27. Stochastic Completion Fields (Mumford; Williams & Jacobs)

  28. Stochastic Completion Fields (Mumford; Williams & Jacobs)

  29. Shortest Path (Hu, Sakoda & Pavlidis)

  30. Snakes (Kass, Witkin & Terzopolous) • Given a curve Г(s)=(x(s),y(s)), define:

  31. Snakes: Curve Evolution

  32. Snakes: Curve Evolution

  33. Thresholding

  34. Histogram

  35. Thresholding

  36. Thresholding 125 156 99

  37. Image Segmentation

  38. Camouflage

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