We present an algorithm to segment imagesequences from motion information. A dense vector field estimated by a Wiener-based pel-recursive method represents the key to separate a viewed scene into regions with differe...
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ISBN:
(纸本)0819424358
We present an algorithm to segment imagesequences from motion information. A dense vector field estimated by a Wiener-based pel-recursive method represents the key to separate a viewed scene into regions with different apparent displacement, according to a four-parameter motion model. A preprocessing stage using mathematical morphology enhances pel-recursive motion estimation. The proposed segmentation model, based on Markov Random Fields theory (MRF), considers -besides the motion field- other information sources (gray-level, intensity edges, non-compensated pixels) that help describe the problem more accurately. The maximum a posteriori criterion (MAP) is used for the optimization of the solution, and performed with a deterministic approach. The complete segmentation algorithm includes inicializing, region numbering and labeling, parameter estimation of the motion model in each region, and optimization of the segmentation field. Results on synthetic and real sequences are shown.
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