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Segmentation and Boundary Detection Using Multiscale Intensity Measurements

Learn about segmentation and boundary detection using multiscale intensity measurements for accurate image processing. Explore the technique of weighted aggregation, normalized cuts, and hierarchical structures.

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Segmentation and Boundary Detection Using Multiscale Intensity Measurements

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  1. Segmentation and Boundary DetectionUsing Multiscale Intensity Measurements Eitan Sharon, Meirav Galun, Ronen Basri, Achi Brandt Dept. of Computer Science and Applied Mathematics The Weizmann Institute of Science

  2. Image Segmentation Eitan Sharon - Weizmann Institute

  3. Local Uncertainty Eitan Sharon - Weizmann Institute

  4. Global Certainty Eitan Sharon - Weizmann Institute

  5. Local Uncertainty Eitan Sharon - Weizmann Institute

  6. Global Certainty Eitan Sharon - Weizmann Institute

  7. Coarse Measurements for Texture Eitan Sharon - Weizmann Institute

  8. A Chicken and Egg Problem Problem: Coarse measurements mix neighboring statistics Solution: support of measurements is determined as the segmentation process proceeds Eitan Sharon - Weizmann Institute

  9. Segmentation by Weighted Aggregation • Normalized-cuts measure in graphs • Complete hierarchy in linear time • Use multiscale measures of intensity, texture, shape, and boundary integrity Eitan Sharon - Weizmann Institute

  10. Segmentation by Weighted Aggregation • Normalized-cuts measure in graphs • Complete hierarchy in linear time • Use multiscale measures of intensity, texture, shape, and boundary integrity Eitan Sharon - Weizmann Institute

  11. Segmentation by Weighted Aggregation • Normalized-cuts measure in graphs • Complete hierarchy in linear time • Use multiscale measures of intensity, texture, shape and boundary integrity Eitan Sharon - Weizmann Institute

  12. The Pixel Graph Couplings Reflect intensity similarity Low contrast – strong coupling High contrast – weak coupling Eitan Sharon - Weizmann Institute

  13. Hierarchical Graph Eitan Sharon - Weizmann Institute

  14. Hierarchyin SWA Eitan Sharon - Weizmann Institute

  15. Normalized-Cut Measure Eitan Sharon - Weizmann Institute

  16. Normalized-Cut Measure Eitan Sharon - Weizmann Institute

  17. Normalized-Cut Measure Eitan Sharon - Weizmann Institute

  18. Normalized-Cut Measure Minimize: Eitan Sharon - Weizmann Institute

  19. High-energy cut Normalized-Cut Measure Minimize: Eitan Sharon - Weizmann Institute

  20. Normalized-Cut Measure Low-energy cut Minimize: Eitan Sharon - Weizmann Institute

  21. Recursive Coarsening Eitan Sharon - Weizmann Institute

  22. Recursive Coarsening Representative subset Eitan Sharon - Weizmann Institute

  23. Recursive Coarsening For a salient segment : , sparse interpolation matrix Eitan Sharon - Weizmann Institute

  24. Weighted Aggregation aggregate aggregate Eitan Sharon - Weizmann Institute

  25. Segment Detection Eitan Sharon - Weizmann Institute

  26. SWA Detects the salient segments Hierarchical structure Linear in # of points (a few dozen operations per point) Eitan Sharon - Weizmann Institute

  27. Coarse-Scale Measurements • Average intensities of aggregates • Multiscale intensity-variances of aggregates • Multiscale shape-moments of aggregates • Boundary alignment between aggregates Eitan Sharon - Weizmann Institute

  28. Adaptive vs. Rigid Measurements Original Averaging Geometric Our algorithm - SWA Eitan Sharon - Weizmann Institute

  29. Adaptive vs. Rigid Measurements Original Interpolation Geometric Our algorithm - SWA Eitan Sharon - Weizmann Institute

  30. Recursive Measurements: Intensity intensity of pixel i aggregate average intensity of aggregate Eitan Sharon - Weizmann Institute

  31. Use Averages to Modify the Graph Eitan Sharon - Weizmann Institute

  32. Use Averages to Modify the Graph Eitan Sharon - Weizmann Institute

  33. Texture Examples Eitan Sharon - Weizmann Institute

  34. Isotropic and Oriented Filters A brief tutorial Textons by K-Means Malik et al IJCV2001 Eitan Sharon - Weizmann Institute

  35. Isotropic Texture in SWA Intensity Variance Isotropic Texture of aggregate – average of variances in all scales Eitan Sharon - Weizmann Institute

  36. Isotropic Texture in SWA Intensity Variance Isotropic Texture of aggregate – average of variances in all scales Eitan Sharon - Weizmann Institute

  37. Isotropic Texture in SWA Intensity Variance Isotropic Texture of aggregate – average of variances in all scales Eitan Sharon - Weizmann Institute

  38. Oriented Texture in SWA with Meirav Galun Shape Moments • center of mass • width • length • orientation Oriented Texture of aggregate – orientation, width and length in all scales Eitan Sharon - Weizmann Institute

  39. Gestalt – Perceptual Grouping Shashua and Ullman ICCV 1988: A brief Tutorial • Group curves by: • Proximity • Co-linearity Sharon, Brandt, Basri PAMI 2000: Eitan Sharon - Weizmann Institute

  40. Boundary Integrity in SWA Eitan Sharon - Weizmann Institute

  41. Sharpen the Aggregates • Top-down Sharpening: • Expand core • Sharpen boundaries Eitan Sharon - Weizmann Institute

  42. Hierarchyin SWA Eitan Sharon - Weizmann Institute

  43. images on a pentium III 1000MHz PC: Experiments • Our SWA algorithm (CVPR’00 + CVPR’01) • run-time: 5-10 seconds. • Normalized cuts (Shi and Malik, PAMI’00; Malik et al., IJCV’01) • run-time: about 10-15 minutes. • Software courtesy of Doron Tal, UC Berkeley. Eitan Sharon - Weizmann Institute

  44. Isotropic Texture - Horse I Our Algorithm (SWA) Normalized Cuts Eitan Sharon - Weizmann Institute

  45. Isotropic Texture - Horse II Our Algorithm (SWA) Normalized Cuts Eitan Sharon - Weizmann Institute

  46. Isotropic Texture - Tiger Our Algorithm (SWA) Normalized Cuts Eitan Sharon - Weizmann Institute

  47. Isotropic Texture - Butterfly Our Algorithm (SWA) Normalized Cuts Eitan Sharon - Weizmann Institute

  48. Isotropic Texture - Leopard Our Algorithm (SWA) Eitan Sharon - Weizmann Institute

  49. Isotropic Texture - Dalmatian Dog Our Algorithm (SWA) Eitan Sharon - Weizmann Institute

  50. Isotropic Texture - Squirrel Our Algorithm (SWA) Normalized Cuts Eitan Sharon - Weizmann Institute

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