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A Markovian Model For Contour Grouping

Sabine Urago 1 Josiane Zerubia 1 Marc Berthod 1
1 PASTIS - Scene Analysis and Symbolic Image Processing
CRISAM - Inria Sophia Antipolis - Méditerranée
Abstract : In order to interpret and analyse a scene, determining the contours is a fundamental step. Classical methods of contour extration do not always allow the detection of all the controus. We notice, for exemple, that the contours obtained by a Canny-Deriche filter have some gaps, especially at corners or at T-junctions. In short, the boundaries which are detected are not always closed. In this report, we present an algorith that restores incomplete contours. We model the image by Markov Random Fields and we define the Gibbs Distribution associated with it. In order to complete the contours, several criteria are defined and introduced in an energy function, which has to be optimized. The deterministic ICM "Iterated Conditional Mode" relaxation algorithm is implemented to minimize this energy function. The result is a contour image consisting of closed contours. This method has been tested on different images which present different types of difficulties (indoors, outdoors, satellite (SPOT), industrial and medical images).
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https://hal.inria.fr/inria-00074550
Contributor : Rapport de Recherche Inria <>
Submitted on : Wednesday, May 24, 2006 - 3:44:31 PM
Last modification on : Saturday, January 27, 2018 - 1:31:30 AM
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  • HAL Id : inria-00074550, version 1

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Sabine Urago, Josiane Zerubia, Marc Berthod. A Markovian Model For Contour Grouping. [Research Report] RR-2122, INRIA. 1994. ⟨inria-00074550⟩

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