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Joint Appearance and Deformable Shape for Nonparametric Segmentation

  • Sylvain Boltz
  • Éric Debreuve
  • Michel Barlaud
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4814)

Abstract

This paper deals with region-of-interest (ROI) segmentation in video sequences. The goal is to determine in one frame the region which best matches, in terms of a similarity measure, a ROI defined in a reference frame. A similarity measure can combine color histograms and geometry information into a joint PDF. Geometric information are basically interior region coordinates. We propose a system of shape coordinates constant under shape deformations. High-dimensional color-geometry PDF estimation being a difficult problem, measures based on these PDF distances may lead to an incorrect match. Instead, we use an estimator for Kullback-Leibler divergence efficient for high dimensional PDFs. The distance is expressed from the samples using the kth-nearest neighbor framework (kNN). We plugged this distance into active contour framework using shape derivative. Segmentation results on both rigid and articulated objects showed promising results.

Keywords

Spatial Feature Active Contour Probability Density Function Cross Entropy Shape Derivative 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Sylvain Boltz
    • 1
  • Éric Debreuve
    • 1
  • Michel Barlaud
    • 1
  1. 1.Laboratoire I3S, Université de Nice-Sophia Antipolis, CNRS, 2000 route des Lucioles, 06903 Sophia AntipolisFrance

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