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A Particle Filter Framework for Contour Detection

Nicolas Widynski and Max Mignotte

Department of Computer Science and Operations Research (DIRO), University of Montreal, C.P. 6128, succ. Centre-Ville, Montreal, Quebec, H3C 3J7, Canada
widynski@iro.umontreal.ca
mignotte@iro.umontreal.ca

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.

LNCS 7572, p. 780 ff.

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