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Image Segmentation

Image Segmentation. The Pixel Graph. Couplings {W ij } Reflect intensity similarity. Low contrast – strong coupling. High contrast – weak coupling. Segmentation minimize cut coupling. Low-energy cut. Normalized-Cut Measure. Minimize:. Coarsening the Minimization Problem.

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Image Segmentation

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  1. Image Segmentation

  2. The Pixel Graph Couplings {Wij} Reflect intensity similarity Low contrast – strong coupling High contrast – weak coupling

  3. Segmentationminimize cut coupling Low-energy cut

  4. Normalized-Cut Measure Minimize:

  5. Coarsening the Minimization Problem

  6. Coarsening – Choosing a Coarse Grid Representative subset

  7. Weighted Aggregation aggregate aggregate

  8. Hierarchical Graph

  9. Hierarchyin SWA

  10. Segmentation by Weighted Aggregation Detects the min-normalized-cut salient segments Linear in # of points (a few dozen operations per point) Orders of magnitude faster than…

  11. Image Segmentation

  12. Use Averages to Modify the Graph

  13. Coarse-Scale Measurements • Average intensities of aggregates

  14. Image Segmentation

  15. Aggregate Measurements • Average intensities • Intensity variances (multiscale) • Direction alignment • Boundary alignment • Average “hair” orientation

  16. SWA

  17. Specialized segmentation:Detecting Lesions Tagged Our results Data: Filippi

  18. Our Algorithm (SWA) Ncuts Isotropic texture SWA

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