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

Mikaël Rousson (1) , Rachid Deriche

1 ODYSSEE - Computer and biological vision

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.

Mots clés: LEVEL SET THEORY; COLOR IMAGE SEGMENTATION; ADAPTATIVE IMAGE SEGMENTATION; SHAPE GRADIENT

Domaines: Autre [cs.OH]

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