Statistical multiregion snake-based segmentation

We have recently proposed a new statistical approach based on active contours (snakes) for the segmentation of a unique object in an image. In this article, we address the case of an image composed of several different regions. We propose an extension of snake to a deformable partition of the image with a fixed number of regions. This active grid allows a semi-supervised segmentation of the image.

[1]  Christophe Chesnaud,et al.  Statistical Region Snake-Based Segmentation Adapted to Different Physical Noise Models , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  J. Goodman Statistical Properties of Laser Speckle Patterns , 1963 .

[3]  Geir Storvik,et al.  A Bayesian Approach to Dynamic Contours Through Stochastic Sampling and Simulated Annealing , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  P Réfrégier,et al.  Optimal snake-based segmentation of a random luminance target on a spatially disjoint background. , 1996, Optics letters.

[5]  Charles Kervrann,et al.  A hierarchical statistical framework for the segmentation of deformable objects in image sequences , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[6]  C. Oliver,et al.  Optimum edge detection in SAR , 1996 .

[7]  Isabelle Herlin,et al.  A deformable region model using stochastic processes applied to echocardiographic images , 1992, Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[8]  C. Robert The Bayesian choice : a decision-theoretic motivation , 1996 .

[9]  Olivier Germain,et al.  Edge location in SAR images: performance of the likelihood ratio filter and accuracy improvement with an active contour approach , 2001, IEEE Trans. Image Process..

[10]  Philippe Marthon,et al.  Optimal edge detection and edge localization in complex SAR images with correlated speckle , 1999, IEEE Trans. Geosci. Remote. Sens..