Image segmentation by a contrario simulation

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. © 2009 Elsevier Ltd. All rights reserved.
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Submitted on : Thursday, July 25, 2013 - 9:31:39 AM
Last modification on : Wednesday, July 3, 2019 - 10:48:04 AM

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Nicolas Burrus, Thierry Bernard, Jean-Michel Jolion. Image segmentation by a contrario simulation. Pattern Recognition, Elsevier, 2009, 42 (7), pp.1520-1532. ⟨10.1016/j.patcog.2009.01.003⟩. ⟨hal-00847910⟩

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