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Multi-Scale Contour Extraction Based on Natural Image Statistics

Investigating object boundary extraction using probabilistic grouping and spatial priors derived from natural scene statistics. The algorithm refines contours through multi-scale grouping with an emphasis on proximity and good continuation cues. Experimental results compare the method against ground-truth data and heuristic searches for error analysis and future improvements.

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Multi-Scale Contour Extraction Based on Natural Image Statistics

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  1. Multi-Scale Contour Extraction Based on Natural Image Statistics Francisco J. Estrada and James H. Elder 5th IEEE Computer Society Workshopon Perceptual Organization in Computer Vision Centre for Vision Research

  2. Finding object boundaries… How Hard Can It Be?

  3. Finding object boundaries… How Hard Can It Be? The search-space is very large

  4. Finding object boundaries… How Hard Can It Be? The search-space is very large Local structure can easily dominate

  5. Proximity • Good continuation • Similarity Probabilistic Grouping • Estimate grouping probability • Use natural scene statistics • Gestalt grouping cues

  6. Probabilistic Grouping • Probability distributions

  7. Relate pairs of consecutive tangents Provide a prior for grouping Probabilistic Grouping • Constructive algorithm • Expand • Prune

  8. Input tangents Coarse-scale contour Spatial prior Spatial Prior

  9. Coarse-scale contour Noisy, irregular Fourier Descriptor Spatial Prior

  10. Coarse-scale contour Smooth Fourier contour Project to fine scale Spatial Prior

  11. Spatial Prior • Measure: • - Distance from Fourier contour • - Difference in angle

  12. Spatial Prior

  13. Input tangents Boundary Energy (Martin et al. 2002) P(grouped|BE) Boundary Energy • Additional object cue

  14. (*) Using the geometric mean Algorithm Summary • Group at coarsest scale Repeat: • Select the N contours to refine (*) • Group at fine scale using spatial prior • Additional run without spatial prior

  15. Experimental Results • 20 images from the BSD • Results reported for: • * (GND) Ground-truth • * (MS) Multi-scale grouping • * (SS) Single-scale grouping • * (RC) Ratio Contour (Wang et al. 2005) • * (EJ) Heuristic search (Estrada & Jepson 2004)

  16. Experimental Results

  17. Detected Overlay Ground-Truth Error = + Error Measure for Comparison

  18. Experimental Results

  19. Experimental Results

  20. Further considerations • Use of appearance information (colour, texture, etc.) • Contour selection • Use of prior shape models • Improve evaluation framework

  21. Thanks! questions please?

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