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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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