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Fast Unsupervised Segmentation Using Active Contours and Belief Functions

Abstract : In this paper, we study Active Contours AC based globally segmentation for vector valued images using evidential Kullback-Leibler KL distance. We investigate the evidential framework to fuse multiple features issued from vector-valued images. This formulation has two main advantages: 1 by the combination of foreground/background issued from the multiple channels in the same framework. 2 the incorporation of the heterogeneous knowledge and the reduction of the imprecision due to the noise. The statistical relation between the image channels is ensured by the Dempster-Shafer rule. We illustrate the performance of our segmentation algorithm using some challenging color and textured images.
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https://hal-uphf.archives-ouvertes.fr/hal-03614737
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Submitted on : Monday, March 21, 2022 - 9:47:12 AM
Last modification on : Tuesday, March 22, 2022 - 3:34:36 AM

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Foued Derraz, Laurent Peyrodie, Abdelmalik Taleb-Ahmed, Miloud Boussahla, Gérard Forzy. Fast Unsupervised Segmentation Using Active Contours and Belief Functions. 15th International Conference, CAIP 2013, Aug 2013, York, United Kingdom. pp.278-285, ⟨10.1007/978-3-642-40261-6_33⟩. ⟨hal-03614737⟩

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