Abstract
In this paper we address the problem of segmentation in image sequences using region-based active contours and level set methods. We propose a novel method for variational segmentation of image sequences containing nonrigid, moving objects. The method is based on the classical Chan-Vese model augmented with a novel frame-to-frame interaction term, which allow us to update the segmentation result from one image frame to the next using the previous segmentation result as a shape prior. The interaction term is constructed to be pose-invariant and to allow moderate deformations in shape. It is expected to handle the appearance of occlusions which otherwise can make segmentation fail. The performance of the model is illustrated with experiments on synthetic and real image sequences.
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Bresson, X., Vandergheynst, P., & Thiran, J. P. (2006). A variational model for object segmentation using boundary information and shape prior driven by the Mumford-Shah functional. International Journal of Computer Vision, 68(2), 145–162.
Caselles, V., Kimmel, R., & Sapiro, G. (1997). Geodesic active contours. International Journal of Computer Vision, 22(1), 61–79.
Chan, T., & Vese, L. (2001). Active contour without edges. IEEE Transactions on Image Processing, 10(2), 266–277.
Chan, T., & Zhu, W. (2005). Level set based prior segmentation. Proceeding CVPR, 2005(2), 1164–1170.
Chen, Y., Tagare, H. D., Thiruvenkadam, S., Huang, F., Wilson, D., Gopinath, K. S., Briggs, R. W., & Geiser, E. A. (2002). Using prior shapes in geometric active contours in a variational framework. International Journal of Computer Vision, 50(3), 315–328.
Cremers, D. (2006). Dynamical statistical shape priors for level set-based tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(8), 1262–1273.
Cremers, D., & Soatto, S. (2003). A pseudo-distance for shape priors in level set segmentation. In Faugeras, O., & Paragios, N. (Eds.), 2nd IEEE workshop on variational, geometric and level set methods in computer vision.
Cremers, D., Osher, S., & Soatto, S. (2006a). Kernel density estimation and intrinsic alignment for shape priors in level set segmentation. International Journal of Computer Vision, 69(3), 335–351.
Cremers, D., Sochen, N., & Schnörr, C. (2006b). A multiphase dynamic labeling model for variational recognition-driven image segmentation. International Journal of Computer Vision, 66(1), 67–81.
Delfour, M. C., & Zolesio, J. P. (2001). Shapes and geometries. Analysis, differential calculus, and optimization. Advances in design and control. SIAM.
Fundana, K., Overgaard, N., & Heyden, A. (2007). Variational segmentation of image sequences using deformable shape priors. In LNCS : Vol. 4522. SCIA 2007 (pp. 31–40). Berlin: Springer.
Gentile, C., Camps, O., & Sznaier, M. (2004). Segmentation for robust tracking in the presence of severe occlusion. IEEE Transactions on Image Processing, 13(2), 166–178.
Kass, M., Witkin, A., & Terzopoulos, D. (1988). Snakes: Active contour models. International Journal of Computer Vision, 321–331.
Leventon, M., Grimson, W., & Faugeras, O. (2000). Statistical shape influence in geodesic active contours. In Proc. int’l conf. computer vision and pattern recognition (pp. 316–323).
Lucas, B. D., & Kanade, T. (1981). An iterative image registration technique with an application to stereo vision. In Image understanding workshop (pp. 121–130).
Moelich, M., & Chan, T. (2003). Tracking objects with the Chan-Vese algorithm (Technical Report 03-14). Department of Mathematics, UCLA.
Mumford, D., & Shah, J. (1988). Optimal approximations by piecewise smooth functions and variational problems. Communication on Pure and Applied Mathematics, XLII(5), 577–685.
Osher, S., & Fedkiw, R. (2003). Level set methods and dynamic implicit surfaces. New York: Springer.
Paragios, N., & Deriche, R. (2000). Geodesic active contours and level set methods for the detection and tracking of moving objects. IEEE Transactions on PAMI, 22(3), 266–280.
Paragios, N., & Deriche, R. (2005). Geodesic active regions and level set methods for motion estimation and tracking. Computer Vision and Image Understanding, 97, 259–282.
Paragios, N., Rousson, M., & Ramesh, V. (2003). Matching distance functions: a shape-to-area variational approach for global-to-local registration. In A. Heyden et al. (Eds.), LNCS : Vol. 2351. ECCV 2002 (pp. 775–789). Berlin: Springer.
Riklin-Raviv, T., Kiryati, N., & Sochen, N. (2007). Prior-based segmentation and shape registration in the presence of perspective distortion. International Journal of Computer Vision, 72(3), 309–328.
Rousson, M., & Cremers, D. (2005). Efficient kernel density estimation of shape and intensity priors for level set segmentation. In J. S. Duncan & G. Gerig (Eds.), Lecture notes in computer science : Vol. 3750. MICCAI (2) (pp. 757–764). Berlin: Springer.
Rousson, M., & Paragios, N. (2002). Shape priors for level set representations. In A. Heyden et al. (Eds.), LNCS : Vol. 2351. ECCV 2002 (pp. 78–92). Berlin: Springer.
Rousson, M., & Paragios, N. (2008). Prior knowledge, level set representations and visual grouping. International Journal of Computer Vision, 76(3), 231–243.
Solem, J. E., & Overgaard, N. C. (2005). A geometric formulation of gradient descent for variational problems with moving surfaces. In R. Kimmel, N. Sochen, & J. Weickert (Eds.), LNCS : Vol. 3459. Scale-space 2005 (pp. 419–430). Berlin: Springer.
Thiruvenkadam, S. R., Chan, T. F., & Hong, B. W. (2007). Segmentation under occlusions using selective shape prior. In LNCS : Vol. 4485. Scale space and variational methods in computer vision (pp. 191–202). Berlin: Springer.
Tsai, A., Yezzy, A., Wells, W., Tempany, C., Tucker, D., Fan, A., Grimson, W. W., & Willsky, A. (2003). A shape-based approach to the segmentation of medical imagery using level sets. IEEE Transactions on Medical Imaging, 22(2), 137–154.
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Fundana, K., Overgaard, N.C. & Heyden, A. Variational Segmentation of Image Sequences Using Region-Based Active Contours and Deformable Shape Priors. Int J Comput Vis 80, 289–299 (2008). https://doi.org/10.1007/s11263-008-0160-6
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DOI: https://doi.org/10.1007/s11263-008-0160-6