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dc.contributor.authorPenedo, M. G.
dc.contributor.authorCohen, Laurent D.
HAL ID: 738939
dc.contributor.authorOrtega, M.
dc.contributor.authorBarreira, Noellia
dc.subject3D image segmentationen
dc.subjectTopological active volumesen
dc.subjectAdaptive topologyen
dc.titleTopological active volumes: A topology-adaptive deformable model for volume segmentationen
dc.typeArticle accepté pour publication ou publié
dc.contributor.editoruniversityotherUniversity of A Coruña, Department of Computer Science;Espagne
dc.description.abstractenThis paper proposes a generic methodology for segmentation and reconstruction of volumetric datasets based on adeformablemodel, the topologicalactivevolumes (TAV). This model, based on a polyhedral mesh, integrates features of region-based and boundary-based segmentation methods in order to fit the contours of the objects and model its inner topology. Moreover, it implements automatic procedures, the so-called topological changes, that alter the mesh structure and allow the segmentation of complex features such as pronounced curvatures or holes, as well as the detection of several objects in the scene. This work presents the TAV model and the segmentation methodology and explains how the changes in the TAV structure can improve the adjustment process. In particular, it is focused on the increase of the mesh density in complex image areas in order to improve the adjustment to object surfaces. The suitability of the mesh structure and the segmentation methodology is analyzed and the accuracy of the proposed model is proved with both synthetic and real images.en
dc.relation.isversionofjnlnamePattern Recognition
dc.subject.ddclabelIntelligence artificielleen

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