TY - JOUR
T1 - A region-based MRF model for unsupervised segmentation of moving objects in image sequences
AU - Tsaig, Yaakov
AU - Averbuch, Amir
PY - 2001
Y1 - 2001
N2 - 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 a 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.
AB - 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 a 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.
UR - http://www.scopus.com/inward/record.url?scp=0035683380&partnerID=8YFLogxK
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AN - SCOPUS:0035683380
SN - 1063-6919
VL - 1
SP - I889-I896
JO - Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
JF - Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
T2 - 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Y2 - 8 December 2001 through 14 December 2001
ER -