Spatio-temporal Prior Shape Constraint for Level Set Segmentation

This paper exposes a novel formulation of prior shape constraint incorporation for the level set segmentation of objects from corrupted images. Applicable to variational frameworks, the proposed scheme consists in weighting the prior shape constraint by a function of time and space to overcome local minima issues of the energy functional. Pose parameters which make the prior shape constraint invariant from global transformations are estimated by the downhill simplex algorithm, which is more tractable and robust than the traditional gradient descent. The proposed scheme is simple, easy to implement and can be generalized to any variational approach incorporating a single prior shape. Results illustrated with different kinds of images demonstrate the efficiency of the method.

[1]  Hongbin Zha,et al.  A fast narrow band method and its application in topology-adaptive 3D modeling , 2002, Object recognition supported by user interaction for service robots.

[2]  Timothy F. Cootes,et al.  Active Shape Models-Their Training and Application , 1995, Comput. Vis. Image Underst..

[3]  Yunmei Chen,et al.  On the incorporation of shape priors into geometric active contours , 2001, Proceedings IEEE Workshop on Variational and Level Set Methods in Computer Vision.

[4]  Yunmei Chen,et al.  Using Prior Shapes in Geometric Active Contours in a Variational Framework , 2002, International Journal of Computer Vision.

[5]  Demetri Terzopoulos,et al.  Snakes: Active contour models , 2004, International Journal of Computer Vision.

[6]  Alex M. Andrew,et al.  Level Set Methods and Fast Marching Methods: Evolving Interfaces in Computational Geometry, Fluid Mechanics, Computer Vision, and Materials Science (2nd edition) , 2000 .

[7]  Rachid Deriche,et al.  Geodesic Active Regions: A New Framework to Deal with Frame Partition Problems in Computer Vision , 2002, J. Vis. Commun. Image Represent..

[8]  W. Eric L. Grimson,et al.  Model-based curve evolution technique for image segmentation , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[9]  Daniel Cremers,et al.  Towards Recognition-Based Variational Segmentation Using Shape Priors and Dynamic Labeling , 2003, Scale-Space.

[10]  Nikos Paragios,et al.  Shape Priors for Level Set Representations , 2002, ECCV.

[11]  J. Sethian,et al.  Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations , 1988 .

[12]  Fabrice Heitz,et al.  Geometric shape priors for region-based active contours , 2003, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).

[13]  Tony F. Chan,et al.  Level set based shape prior segmentation , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[14]  Nikos Paragios,et al.  Matching Distance Functions: A Shape-to-Area Variational Approach for Global-to-Local Registration , 2002, ECCV.

[15]  B. Serra,et al.  DIGITAL BUILDING MAP REFINEMENT FROM KNOWLEDGE-DRIVEN ACTIVE CONTOURS AND VERY HIGH RESOLUTION OPTICAL IMAGERY , 2005 .

[16]  O. Faugeras,et al.  Statistical shape influence in geodesic active contours , 2002, 5th IEEE EMBS International Summer School on Biomedical Imaging, 2002..

[17]  Stefano Soatto,et al.  A Pseudo-distance for Shape Priors in Level Set Segmentation , 2003 .

[18]  James S. Duncan,et al.  Boundary Finding with Parametrically Deformable Models , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[19]  John A. Nelder,et al.  A Simplex Method for Function Minimization , 1965, Comput. J..

[20]  Daniel Cremers,et al.  Kernel Density Estimation and Intrinsic Alignment for Knowledge-Driven Segmentation: Teaching Level Sets to Walk , 2004, DAGM-Symposium.

[21]  Daniel Cremers,et al.  Diffusion Snakes: Introducing Statistical Shape Knowledge into the Mumford-Shah Functional , 2002, International Journal of Computer Vision.