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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© 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
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