Image segmentation by a contrario simulation |
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Authors: | Nicolas Burrus [Author Vitae] Thierry M. Bernard [Author Vitae] [Author Vitae] |
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Affiliation: | a ENSTA—UEI, 32 Boulevard Victor, 75015 Paris, France b Université de Lyon, INSA Lyon, LIRIS, UMR CNRS 5205, 69621 Villeurbanne Cedex, France |
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Abstract: | Segmenting an image into homogeneous regions generally involves a decision criterion to establish whether two adjacent regions are similar. Decisions should be adaptive to get robust and accurate segmentation algorithms, avoid hazardous a priori and have a clear interpretation. We propose a decision process based on a contrario reasoning: two regions are meaningfully different if the probability of observing such a difference in pure noise is very low. Since the existing analytical methods are intractable in our case, we extend them to allow a mixed use of analytical computations and Monte-Carlo simulations. The resulting decision criterion is tested experimentally through a simple merging algorithm, which can be used as a post-filtering and validation step for existing segmentation methods. |
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Keywords: | Segmentation A contrario reasoning Statistical image processing Monte-Carlo simulation |
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