Multi-Reference Shape Priors for Active Contours

In this paper, we present a new way of constraining the evolution of an active contour with respect to a set of fixed reference shapes. This approach is based on a description of shapes by the Legendre moments computed from their characteristic function. This provides a region-based representation that can handle arbitrary shape topologies. Moreover, exploiting the properties of moments, it is possible to include intrinsic affine invariance in the descriptor, which solves the issue of shape alignment without increasing the number of d.o.f. of the initial problem and allows introducing geometric shape variabilities. Our new shape prior is based on a distance, in terms of descriptors, between the evolving curve and the reference shapes. Minimizing the corresponding shape energy leads to a geometric flow that does not rely on any particular representation of the contour and can be implemented with any contour evolution algorithm. We introduce our prior into a two-class segmentation functional, showing its benefits on segmentation results in presence of severe occlusions and clutter. Examples illustrate the ability of the model to deal with large affine deformation and to take into account a set of reference shapes of different topologies.

[1]  Fabrice Heitz,et al.  Affine-invariant geometric shape priors for region-based active contours , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Anthony J. Yezzi,et al.  Stochastic differential equations and geometric flows , 2002, IEEE Trans. Image Process..

[3]  W. Eric L. Grimson,et al.  A shape-based approach to the segmentation of medical imagery using level sets , 2003, IEEE Transactions on Medical Imaging.

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

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

[6]  Frédéric Precioso,et al.  B-Spline Active Contour with Handling of Topology Changes for Fast Video Segmentation , 2002, EURASIP J. Adv. Signal Process..

[7]  R. Mukundan,et al.  Moment Functions in Image Analysis: Theory and Applications , 1998 .

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

[9]  Miroslaw Pawlak,et al.  On Image Analysis by Moments , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  Scott T. Acton,et al.  Constraining active contour evolution via Lie Groups of transformation , 2004, IEEE Transactions on Image Processing.

[11]  Tao Zhang,et al.  Tracking objects using density matching and shape priors , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[12]  Dimitris N. Metaxas,et al.  Dynamic 3D models with local and global deformations: deformable superquadrics , 1990, [1990] Proceedings Third International Conference on Computer Vision.

[13]  Daniel Cremers,et al.  Shape statistics in kernel space for variational image segmentation , 2003, Pattern Recognit..

[14]  G. Talenti Recovering a function from a finite number of moments , 1987 .

[15]  Tony F. Chan,et al.  Active contours without edges , 2001, IEEE Trans. Image Process..

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

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

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

[19]  Michel Barlaud,et al.  TRACKING VIDEO OBJECTS USING ACTIVE CONTOURS AND GEOMETRIC PRIORS , 2003 .

[20]  Daniel Cremers,et al.  A Multiphase Dynamic Labeling Model for Variational Recognition-driven Image Segmentation , 2005, International Journal of Computer Vision.

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

[22]  Daniel Cremers,et al.  Kernel Density Estimation and Intrinsic Alignment for Shape Priors in Level Set Segmentation , 2006, International Journal of Computer Vision.

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

[24]  M. Teague Image analysis via the general theory of moments , 1980 .

[25]  Soo-Chang Pei,et al.  Image normalization for pattern recognition , 1995, Image Vis. Comput..

[26]  Anuj Srivastava,et al.  Statistical shape analysis: clustering, learning, and testing , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[27]  Xavier Bresson,et al.  A Variational Model for Object Segmentation Using Boundary Information and Shape Prior Driven by the Mumford-Shah Functional , 2006, International Journal of Computer Vision.

[28]  Olivier D. Faugeras,et al.  Image Segmentation Using Active Contours: Calculus of Variations or Shape Gradients? , 2003, SIAM J. Appl. Math..

[29]  Nahum Kiryati,et al.  Unlevel-Sets: Geometry and Prior-Based Segmentation , 2004, ECCV.

[30]  Guido Gerig,et al.  Segmentation of 2-D and 3-D objects from MRI volume data using constrained elastic deformations of flexible Fourier contour and surface models , 1996, Medical Image Anal..

[31]  S. Osher,et al.  Algorithms Based on Hamilton-Jacobi Formulations , 1988 .

[32]  Guido Gerig,et al.  Segmentation of 3D Objects from MRI Volume Data Using Constrained Elastic Deformations of Flexible Fourier Surface Models , 1995, CVRMed.

[33]  Fabrice Heitz,et al.  Affine-Invariant Multi-reference Shape Priors for Active Contours , 2006, ECCV.