Segmentation and Boundary Detection Using Multiscale Measurements Ronen Basri Achi Brandt Meirav Galun Eitan Sharon.

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Presentation transcript:

Segmentation and Boundary Detection Using Multiscale Measurements Ronen Basri Achi Brandt Meirav Galun Eitan Sharon

Eitan Sharon - Weizmann Institute Image Segmentation

Eitan Sharon - Weizmann Institute Local Uncertainty

Eitan Sharon - Weizmann Institute Global Certainty

Eitan Sharon - Weizmann Institute Local Uncertainty

Eitan Sharon - Weizmann Institute Global Certainty

Eitan Sharon - Weizmann Institute Coarse Measurements for Texture

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

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

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

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

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

Eitan Sharon - Weizmann Institute Hierarchical Graph

Eitan Sharon - Weizmann Institute Hierarchy in SWA

Eitan Sharon - Weizmann Institute Normalized-Cut Measure

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

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

Eitan Sharon - Weizmann Institute Segment Detection

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

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

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

Eitan Sharon - Weizmann Institute Use Averages to Modify the Graph

Eitan Sharon - Weizmann Institute Use Averages to Modify the Graph

Eitan Sharon - Weizmann Institute Texture Examples

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

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

Eitan Sharon - Weizmann Institute Boundary Integrity in SWA

Eitan Sharon - Weizmann Institute Hierarchy in SWA

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

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 minutes. Software courtesy of Doron Tal, UC Berkeley. images on a pentium III 1000MHz PC:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Eitan Sharon - Weizmann Institute Separation of Parts Poissonian u: Δu = 1 u = 0 outside the segment