1 / 66

Segmentation and Perceptual Grouping in Computer Vision

Explore the concepts of segmentation, perceptual organization, and Gestalt principles in computer vision, including techniques like smooth completion, Hough transform, saliency networks, tensor voting, and stochastic completion fields.

jpoole
Télécharger la présentation

Segmentation and Perceptual Grouping in Computer Vision

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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

More Related