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A Variational Framework for Active and Adaptative Segmentation of Vector Valued Images

Mikaël Rousson 1 Rachid Deriche
1 ODYSSEE - Computer and biological vision
DI-ENS - Département d'informatique - ENS Paris, CRISAM - Inria Sophia Antipolis - Méditerranée , ENS-PSL - École normale supérieure - Paris, Inria Paris-Rocquencourt, ENPC - École des Ponts ParisTech
Abstract : During the last few years, many efforts have been done in integrating different informations in a variational framework to segment images. Recent works on curve propagation were able to incorporate stochastic informations and prior knowledge on shapes . The information inserted in these studies is most of the time extracted offline. Meanwhile, other approaches have proposed to extract region information during the segmentation process itself . Following these new approaches and extending the work in to vector-va- lued images, we propose in this paper an entirely variational framework to approach the segmentation problem. Both, the image partition and the statistic- al parameters for each region are unkown. After a brief reminder on recent segmenting methods, we will present a variational formulation obtained from a bayesian model. After that, we will show two different differentiations driving to the same evolution equations. Detailed studies on gray and color images of the 2-phase case will follow. And we will finish on an application to tracking which shows benefits of our dynamical framework.
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Submitted on : Tuesday, May 23, 2006 - 7:43:13 PM
Last modification on : Thursday, March 17, 2022 - 10:08:32 AM
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  • HAL Id : inria-00072073, version 1



Mikaël Rousson, Rachid Deriche. A Variational Framework for Active and Adaptative Segmentation of Vector Valued Images. RR-4515, INRIA. 2002. ⟨inria-00072073⟩



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