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A region-based MRF model for unsupervised segmentation of moving objects in image sequences

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2 Author(s)
Y. Tsaig ; Dept. of Comput. Sci., Tel-Aviv Univ., Israel ; A. Averbuch

This paper addresses the problem of segmentation of moving objects in image sequences, which is of key importance in content-based applications. We transform the problem into a graph labeling problem over a region adjacency graph (RAG), by introducing a Markov random field (MRF) model based on spatio-temporal information. The initial partition is obtained by fast, color-based watershed segmentation. The motion of each region is estimated and validated in a hierarchical framework. A dynamic memory, based on object tracking, is incorporated into the segmentation process to maintain temporal coherence. The performance of the algorithm is evaluated on several real-world image sequences.

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Computer Vision and Pattern Recognition, 2001. CVPR 2001. Proceedings of the 2001 IEEE Computer Society Conference on  (Volume:1 )

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