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A Particle Filter Framework for Contour Detection

  • Nicolas Widynski
  • Max Mignotte
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7572)

Abstract

We investigate the contour detection task in complex natural images. We propose a novel contour detection algorithm which locally tracks small pieces of edges called edgelets. The combination of the Bayesian modeling and the edgelets enables the use of semi-local prior information and image-dependent likelihoods. We use a mixed offline and online learning strategy to detect the most relevant edgelets. The detection problem is then modeled as a sequential Bayesian tracking task, estimated using a particle filtering technique. Experiments on the Berkeley Segmentation Datasets show that the proposed Particle Filter Contour Detector method performs well compared to competing state-of-the-art methods.

Keywords

Particle Filter Hide State Transition Distribution Contour Detection Tracking Procedure 
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 2012

Authors and Affiliations

  • Nicolas Widynski
    • 1
  • Max Mignotte
    • 1
  1. 1.Department of Computer Science and Operations Research (DIRO)University of MontrealMontrealCanada

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