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Markov random fields for catadioptric image processing
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DOI：10.1016/j.patrec.2006.05.007.png)
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Abstract
En 中文
Images obtained with catadioptric sensors contain significant deformations which prevent the direct use of classical image treatments. Thus, Markov random fields (MRF) whose usefulness is now obvious for projective image processing, cannot be used directly on catadioptric images because of the inadequacy of the neighborhood. In this paper, we propose to define a new neighborhood for MRF by using the equivalence theorem developed for central catadioptric sensors. We show the importance of this adaptation for segmentation, image restoration and motion detection. (c) 2006 Elsevier B.V. All rights reserved.
Keywords:
catadioptric vision
Markov random field
neighborhood
equivalent projection
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1.6W
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