Adaptive Color Model for Figure-Ground Segmentation in Dynamic Environments

  • Francesc Moreno-Noguer
  • Alberto Sanfeliu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3287)

Abstract

In this paper we propose a new technique to perform figure-ground segmentation in image sequences of scenarios with varying illumination conditions. Most of the algorithms in the literature that adapt color, assume smooth color changes over time. On the contrary, our technique formulates multiple hypotheses about the next state of the color distribution (modelled with a Mixture of Gaussians -MoG-), and validates them taking into account shape information of the object. The fusion of shape and color is done in a stage denominated ’sample concentration’, that we introduce as a final step to the classical CONDENSATION algorithm. The multiple hypotheses generation, allows for more robust adaptions procedures, and the assumption of gradual change of the lighting conditions over time is no longer necessary.

Keywords

Gaussian Mixture Model Tracking Algorithm Gaussian Component Object Position Color Distribution 
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 2004

Authors and Affiliations

  • Francesc Moreno-Noguer
    • 1
  • Alberto Sanfeliu
    • 1
  1. 1.Institut de Robòtica i Informàtica IndustrialUPC-CSICBarcelonaSpain

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