Abstract
Motion segmentation needs to estimate the parameters of motion and its supporting region. The usual problem in determining the supporting region is how to obtain a complete spatial consistence. On the basis of maximum posterior marginal probability (MPM-MAP) algorithm this paper presents a new algorithm based on region shrinking to locate the supporting area. First the motion parameters are estimated by MPM-MAP algorithm. In this algorithm pixels of maximum probabilities belonging to a motion are considered to be preselected pixels for supporting area. Then the region shrinking algorithm is used to determine the region of maximum density of the preselected pixels to be the range of supporting area. Finally the active contour based on gradient vector flow (GVF) is adopted to obtain the accurate shape of supporting region. This method obtains a solid region to be supporting area of a motion and extracts the accurate shape of moving objects, so it offers a better way in motion segmentation to solve the problem of spatial continuity.
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References
Wang, J.Y.A., Adelson, E.H.: Representing Moving Images with Layers. IEEE Trans. Image Process. 3, 625–638 (1994)
Weiss, Y.: Smoothness in Layers: Motion Segmentation Using Nonparametric Mixture Estimation. In: Proc. IEEE Conf. Comput., pp. 520–527. Vision Pattern Recogn. (1997)
Weiss, Y., Adelson, E.H.: A Unified Mixture Framework for Motion Segmentation: Incorporing Spatial Coherence and Estimating the Number of Model. In: Proc. IEEE Conf. Comput., pp. 321–326. Vision Pattern Recogn. (1996)
Celeux, G., Forbes, F.: EM Procedures Using Mean Field-Like Approximations for Markov Model-Based Image Segmentation. Pattern Recognition 36, 131–144 (2003)
Calderon, F., Marroquin, J.L.: The MPM–MAP Algorithm for Motion Segmentation. Computer Vision and Image Understanding 95, 165–183 (2004)
Marroquin, J.L., Velasco, F., Rivera, M., Nakamura, M.: Gauss-Markov Measure Field Model for Low-Level Vision. IEEE Trans. PAMI 23(4), 337–348 (2001)
Black, M., Anandan, P.: The Robust Estimation of Multiple Motions: Parametric and Piecewise-Smooth Flow Fields. Computer Vision and Imag Understanding 63(1), 75–104 (1996)
Blake, A., Asard, M.: Active Contours. Springer, New York (1998)
Xu, C., Prince, J.: Gradient Vector Flow – A New External Force for Snakes. In: IEEE Proc. Conference on Computer Vision and Pattern. Recognition (CVPR 1997) (1997)
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© 2006 Springer-Verlag Berlin Heidelberg
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Li, Z., Huang, F., Liu, Y. (2006). A Method of Motion Segmentation Based on Region Shrinking. In: Corchado, E., Yin, H., Botti, V., Fyfe, C. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2006. IDEAL 2006. Lecture Notes in Computer Science, vol 4224. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11875581_33
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DOI: https://doi.org/10.1007/11875581_33
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45485-4
Online ISBN: 978-3-540-45487-8
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