Motion Imagery Segmentation Via PDE

Separating moving objects from the background in a video clip is known as the image sequence segmentation problem. In recent years, it has attracted considerable interest due to its applications in a wide range of areas. These include object-based video compression (e.g., MPEG4), video surveillance and monitoring, automatic target detection and tracking, and the analysis of medical and other scientific image sequences. Most previous work in image sequence segmentation can be classified into three approaches: 1) intra-frame segmentation, 2) motion field clustering/ segmentation, and 3) frame differencing. In the first approach (e.g., see [1]), each frame in an image sequence is segmented independently (i.e., intra-frame) into regions of homogeneous intensity or texture, using traditional image segmentation techniques. Then regions in consecutive frames are matched and tracked. This would work well if intra-frame segmentation produced a small number of regions closely related to real-world objects. However, since image segmentation using intensity or texture remains a difficult problem, this approach often encounters difficulties, as many image segmentation techniques produce over-segmentation. In the second approach, a dense motion field is used for segmentation. For example, pixels with similar motion vectors are grouped into regions (e.g., see [2]-[3]). Alternatively, small regions generated from intra-frame segmentation University of Wisconsin-Milwaukee, Milwaukee, WI, USA 2 University of Wisconsin-Milwaukee, Milwaukee, WI, USA

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