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Une méthode de réduction exacte pour la segmentation par graph cuts

Abstract : From now on, graph cuts are a standard in the computer vision community. However, their huge memory consumption remains a challenging problem since underlying graphs contains billions of nodes and even more edges. Except some exact methods [14, 10, 5], the heuristics in the literature can only obtain an approached solution [12, 8]. First, we present a new strategy for reducing exactly these graphs : the graph is built by adding nodes which satisfy locally a given condition and corresponds in a narrow band around the segmented object edges. The experiments presented for segmenting gray-levels and color images highlight low memory usage and show a low distance between segmentations. We also present an application of this method for segmenting lung tumors in CT images.
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Submitted on : Sunday, May 29, 2011 - 9:47:06 PM
Last modification on : Wednesday, October 27, 2021 - 2:11:57 PM
Long-term archiving on: : Friday, November 9, 2012 - 1:56:26 PM


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  • HAL Id : inria-00596724, version 1


Nicolas Lermé, François Malgouyres, Lucas Létocart, Jean-Marie Rocchisani. Une méthode de réduction exacte pour la segmentation par graph cuts. ORASIS - Congrès des jeunes chercheurs en vision par ordinateur, INRIA Grenoble Rhône-Alpes, Jun 2011, Praz-sur-Arly, France. ⟨inria-00596724⟩



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