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Segmentation d'image par simulations a contrario

Résumé : 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 clear justification. We propose a decision process based on an 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 that case, we generalize 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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Submitted on : Monday, April 3, 2017 - 2:50:35 PM
Last modification on : Friday, September 30, 2022 - 11:34:16 AM


  • HAL Id : hal-01500606, version 1


Nicolas Burrus, Thierry Bernard, Jean-Michel Jolion. Segmentation d'image par simulations a contrario. RFIA, Jan 2008, Amiens, France. pp.1-10. ⟨hal-01500606⟩



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