An original approach for motion segmentation using the movement of object boundaries is presented. Our treatment is focused on the case of two motions (background and foreground) and just two frames but can be extended to multiple motions and multiple frames. Segmentation results are presented and some shortcomings of the approach based on global motion models are discussed.

In this paper we address the problem of fast segmenting moving objects in video acquired by moving camera or more generally with a moving background. We present an approach based on a color segmentation followed by a region-merging on motion through Markov Random Fields (MRFs). The technique we propose is inspired to a work of Gelgon and Bouthemy [6], that has been modified to reduce computational cost in order to achieve a fast segmentation (about ten frame per second). To this aim a modified region matching algorithm (namely Partitioned Region Matching) and an innovative arc-based MRF optimization algorithm with a suitable definition of the motion reliability are proposed. Results on both synthetic and real sequences are reported to confirm validity of our solution. 1.

Abstract not found

The problem of segmenting color video sequences is addressed. Boundary motion and occlusion relations expressed by labeling rules are argued to be of key importance for segmentation. Interframe motion of object(s) and background is approximated by a similarity or affine transforms. The computation proceeds in several steps: (1) color gradient edges are fuzzy-clustered according to their motion; (2) boundaries of regions formed by a color segmenter are assigned weights and labeled according to their motion; (3) un-ambiguously moving regions and "conflict" regions are identified by a label relaxation procedure; (4) ambiguities and conflicts are resolved by multiframe analysis.