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With the advance of technology, our demand for video is increasing. As a result, the video application is expanded at the same time. Therefore, to employ the motion video to the full, we have to develop an ideal set of segmental technology as a preprocess. For the segmental technology, the most important is to precisely define the region or the object. A clear definition will be helpful to the research in the regional segmentation. In this thesis, we present a set of video segmentation methods. After introducing a still segmentation method JSEG, we use it to generate our initial single frame segmentation. Then we use Bayesian motion estimation and Markov Random Field to formulate our three iterative stpes between two continuous frames. The first step update the motion fields of video, and the second frame tend to find the occlusion region, we use a heuristic method to substitude the original step for simplification. And after simplification, the final step we modify the region fields, it can be divided into two parts: merging the regions and improving the accurate region boundaries. After simulation with the table tennis and flow garden sequences. Although there are some improvement compared to the original segmentation, the results do not as perfect as what we anticipate. In the future, we can take more frames into account or implement the Bayesian iteration without simplification.
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