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Shape-based analysis on component-graphs for multivalued image processing

Abstract : Connected operators based on hierarchical image models have been increasingly considered for the design of efficient image segmentation and filtering tools in various application fields. Among hierarchical image models, component-trees represent the structure of grey-level images by considering their nested binary level-sets obtained from successive thresholds. Recently, a new notion of component-graph was introduced to extend the component-tree to any grey-level or multi- valued images. The notion of shaping was also introduced as a way to improve the anti-extensive filtering by considering a two-layer component-tree for grey-level image processing. In this article, we study how component-graphs (that extend the component-tree from a spectral point of view) and shapings (that extends the component-tree from a conceptual point of view) can be associated for the effective processing of multival- ued images. We provide structural and algorithmic developments. Although the contributions of this article are mainly theoretical and methodological, we finally provide an illustration example that qualitatively emphasizes the potential use and usefulness of the proposed paradigms for actual image analysis purposes.
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Submitted on : Monday, September 17, 2018 - 10:20:26 PM
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Eloïse Grossiord, Benoît Naegel, Hugues Talbot, Laurent Najman, Nicolas Passat. Shape-based analysis on component-graphs for multivalued image processing. Mathematical Morphology - Theory and Applications, De Gruyter 2019, 3 (1), pp.45-70. ⟨10.1515/mathm-2019-0003⟩. ⟨hal-01695384v3⟩



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