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Image Characterization by Morphological Hierarchical Representations

Abstract : This thesis deals with the extraction of hierarchical and multiscale descriptors on images, in order to interpret, characterize and segment them. It breaks down into two parts.The first part outlines a theoretical and methodological approach for obtaining hierarchical clusterings of the nodes of an edge-weighted graph. In addition, we introduce different approaches to combine hierarchical segmentations. These methods are then applied to graphs representing images and derive different hierarchical segmentation techniques. Finally, we propose a methodology for structuring and studying the space of hierarchies by using the Gromov-Hausdorff distance as a metric.The second part explores several applications of these hierarchical descriptions for images. We expose a method to learn how to automatically extract a segmentation of an image, given a type of images and a score of evaluation for a segmentation. We also propose image descriptors obtained by measuring inter-hierarchical distances, and expose their efficiency on real and simulated data. Finally, we extend the potential applications of these hierarchies by introducing a technique to take into account any spatial prior information during their construction.
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Submitted on : Tuesday, April 9, 2019 - 5:33:08 PM
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Amin Fehri. Image Characterization by Morphological Hierarchical Representations. Information Theory [math.IT]. Université Paris sciences et lettres, 2018. English. ⟨NNT : 2018PSLEM063⟩. ⟨tel-02094613⟩



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