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Segmentation and Boundary Detection Using Multiscale Intensity Measurements Eitan Sharon, Meirav Galun, Ronen Basri, Achi Brandt Dept. of Computer Science.

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Presentation on theme: "Segmentation and Boundary Detection Using Multiscale Intensity Measurements Eitan Sharon, Meirav Galun, Ronen Basri, Achi Brandt Dept. of Computer Science."— Presentation transcript:

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

2 Eitan Sharon - Weizmann Institute Image Segmentation

3 Eitan Sharon - Weizmann Institute Local Uncertainty

4 Eitan Sharon - Weizmann Institute Global Certainty

5 Eitan Sharon - Weizmann Institute Local Uncertainty

6 Eitan Sharon - Weizmann Institute Global Certainty

7 Eitan Sharon - Weizmann Institute Coarse Measurements for Texture

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

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

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

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

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

13 Eitan Sharon - Weizmann Institute Hierarchical Graph

14 Eitan Sharon - Weizmann Institute Hierarchy in SWA

15 Eitan Sharon - Weizmann Institute Normalized-Cut Measure

16 Eitan Sharon - Weizmann Institute Normalized-Cut Measure

17 Eitan Sharon - Weizmann Institute Normalized-Cut Measure

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

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

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

21 Eitan Sharon - Weizmann Institute Recursive Coarsening

22 Eitan Sharon - Weizmann Institute Recursive Coarsening Representative subset

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

24 Eitan Sharon - Weizmann Institute Weighted Aggregation aggregate

25 Eitan Sharon - Weizmann Institute Segment Detection

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

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

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

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

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

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

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

33 Eitan Sharon - Weizmann Institute Texture Examples

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

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

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

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

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

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

40 Eitan Sharon - Weizmann Institute Boundary Integrity in SWA

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

42 Eitan Sharon - Weizmann Institute Hierarchy in SWA

43 Eitan Sharon - Weizmann Institute 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. images on a pentium III 1000MHz PC:

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

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

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

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

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

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

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

51 Eitan Sharon - Weizmann Institute Full Texture - Squirrel Our Algorithm (SWA)Normalized Cuts with Meirav Galun

52 Eitan Sharon - Weizmann Institute Full Texture - Composition Our Algorithm (SWA) with Meirav Galun

53 Eitan Sharon - Weizmann Institute Full Texture – Lion Cub Our Algorithm (SWA) with Meirav Galun

54 Eitan Sharon - Weizmann Institute Full Texture – Polar Bear Our Algorithm (SWA) with Meirav Galun

55 Eitan Sharon - Weizmann Institute Full Texture – Penguin Our Algorithm (SWA) with Meirav Galun

56 Eitan Sharon - Weizmann Institute Full Texture –Leopard Our Algorithm (SWA) with Meirav Galun

57 Eitan Sharon - Weizmann Institute Full Texture - Zebra Our Algorithm (SWA) with Meirav Galun

58 Eitan Sharon - Weizmann Institute Segmentation by Weighted Aggregation Efficient approximation to Ncut-like measures Recursive computation of multiscale measurements Novel adaptive pyramid representing the image

59 Eitan Sharon - Weizmann Institute Matching Experiment (Chen Brestel)

60 Eitan Sharon - Weizmann Institute Matching Experiment (Chen Brestel)

61 Eitan Sharon - Weizmann Institute Matching Experiment (Chen Brestel)

62 Eitan Sharon - Weizmann Institute Experiments – Clustering Silhouettes Data from: Gdalyahu, Weinshall, Werman – CVPR 99 (Also: Domany, Blatt, Gdalyahu, Weinshall)

63 Eitan Sharon - Weizmann Institute Descending order of prominence – objects are fully categorized most prominentless prominent

64 Eitan Sharon - Weizmann Institute Bottom-Up and Top-Down

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