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Algorithmes de correspondance et superpixels pour l’analyse et le traitement d’images

Abstract : This thesis focuses on several aspects of image analysis and processing with non local methods. These methods are based on the redundancy of information that occurs in other images, and use matching algorithms, that are usually patch-based, to extract and transfer information from the example data. These approaches are widely used by the computer vision community, and are generally limited by the computational time of the matching algorithm, applied at the pixel scale, and by the necessity to perform preprocessing or learning steps to use large databases. To address these issues, we propose several general methods, without learning, fast, and that can be easily applied to different image analysis and processing applications on natural and medical images. We introduce a matching algorithm that enables to quickly extract patches from a large library of 3D images, that we apply to medical image segmentation. To use a presegmentation into superpixels that reduces the number of image elements, in a way that is similar to patches, we present a new superpixel neighborhood structure. This novel descriptor enables to efficiently use superpixels in non local approaches. We also introduce an accurate and regular superpixel decomposition method. We show how to evaluate this regularity in a robust manner, and that this property is necessary to obtain good superpixel-based matching performances.
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Submitted on : Thursday, January 11, 2018 - 3:08:32 PM
Last modification on : Tuesday, December 18, 2018 - 3:09:58 AM


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  • HAL Id : tel-01660492, version 2



Remi Giraud. Algorithmes de correspondance et superpixels pour l’analyse et le traitement d’images. Autre [cs.OH]. Université de Bordeaux, 2017. Français. ⟨NNT : 2017BORD0771⟩. ⟨tel-01660492v2⟩



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