Abstract
In this paper we present a new framework for image segmentation using probabilistic multinets. We apply this framework to integration of region-based and contour-based segmentation constraints. A graphical model is constructed to represent the relationship of the observed image pixels, the region labels and the underlying object contour. We then formulate the problem of image segmentation as the one of joint region-contour inference and learning in the graphical model. The joint inference problem is solved approximately in a band area around the estimated contour. Parameters of the model are learned on-line. The fully probabilistic nature of the model allows us to study the utility of different inference methods and schedules. Experimental results show that our new hybrid method outperforms methods that use homogeneous constraints.
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References
Geman, S., Geman, D.: Stochastic Relaxation, Gibbs Distributions and the Bayesian Restoration of Images. In: IEEE Transaction on Pattern Analysis and Machine Intelligence, vol. 6(6) (1984)
Besag, J.E.: On the Statistical Analysis of Dirty Pictures. Journal of the Royal Statistical Society BÂ 48(3) (1986)
Marroquin, J., Mitter, S., Poggio, T.: Probabilistic Solution of Ill-posed Problems in Computational Vision. Journal of American Statistical Association 82(397) (1987)
Freeman, W.T., Pasztor, E.C., Carmichael, O.T.: Learning Low-Level Vision. International Journal of Computer Vision 40(1) (2000)
Boykov, Y.Y., Jolly, M.P.: Interactive Graph Cuts for Optimal Boundary & Region Segmentation of Objects in N-D Images. In: Proceedings of ICCV (2001)
Kass, M., Witkin, A., Terzopoulos, D.: Snakes: Active contour models. International Journal of Computer Vision 1(4) (1987)
Cohen, L.D.: On Active Contour Models and Balloons. Computer Vision, Graphics, and Image Processing: Image Understanding 53(2) (1991)
McInerney, T., Terzopoulos, D.: Topologically Adaptable Snakes. In: Proceedings of ICCV (1995)
Xu, C., Prince, J.L.: Gradient Vector Flow: A New External Force for Snakes. In: Proceedings of CVPR (1997)
Geiger, D., Heckerman, D.: Knowledge representation and inference in similarity networks and Bayesian multinets. Artificial Intelligence 82, 45–74 (1996)
Zhang, Y., Brady, M., Smith, S.: Segmentation of Brain MR Images Through a Hidden Markov Random Field Model and the Expectation-Maximization Algorithm. IEEE Transaction on Medical Imaging 20(1) (2001)
Grenander, U., Miller, M.: Representations of Knowledge in Complex Systems. Journal of the Royal Statistical Society BÂ 56(4) (1994)
Zhu, S.C., Yuille, A.L.: Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation. IEEE Transaction on Pattern Analysis and Machine Intelligence 18(9) (1996)
Chen, T., Metaxas, D.N.: Image Segmentation Based on the Integration of Markov Random Fields and Deformable Models. In: Delp, S.L., DiGoia, A.M., Jaramaz, B. (eds.) MICCAI 2000. LNCS, vol. 1935, pp. 256–265. Springer, Heidelberg (2000)
Huang, R., Pavlovic, V., Metaxas, D.N.: A Graphical Model Framework for Coupling MRFs and Deformable Models. In: Proceedings of CVPR (2004)
Chen, Y., Rui, Y., Huang, T.S.: JPDAF Based HMM for Real-Time Contour Tracking. In: Proceedings of CVPR (2001)
Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann Publishers, San Francisco (1988)
Weiss, Y.: Belief Propagation and Revision in Networks with Loops. Technical Report MIT A.I. Memo 1616 (1998)
Kschischang, F.R., Frey, B.J., Loeliger, H.-A.: Factor Graphs and the Sum-Product Algorithm. IEEE Transactions on Information Theory 47(2) (2001)
Yedidia, J.S., Freeman, W.T., Weiss, Y.: Understanding Belief Propagation and Its Generalizations. In: IJCAI 2001 Distinguished Lecture track (2001)
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Huang, R., Pavlovic, V., Metaxas, D.N. (2005). A Hybrid Framework for Image Segmentation Using Probabilistic Integration of Heterogeneous Constraints. In: Liu, Y., Jiang, T., Zhang, C. (eds) Computer Vision for Biomedical Image Applications. CVBIA 2005. Lecture Notes in Computer Science, vol 3765. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11569541_10
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DOI: https://doi.org/10.1007/11569541_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29411-5
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