Extending the concept of texture to the geometry of a mesh manifold surface, opened the way to the idea of classifying 3D relief patterns as an emerging topic in 3D Computer Vision, with several potential applications. In this paper, we propose an original modelling solution to address this novel task. Following the recent introduction of the LBP computation framework on mesh manifolds(mesh-LBP),wefirstextendthisframeworktothedifferentvariantsof 2DLBPbydefiningmesh-LBPvariants.Thecomplianceoftheseextensionswith theoriginalLBPintermsofuniformityisalsoinvestigated.Then,weproposeda complete framework for relief patterns classification, which performs mesh preprocessing, multi-scale mesh-LBP extraction and descriptors classification. Experimental results on the SHREC’17 dataset showed competitive performance with respect to state of the art solutions.
Defining mesh-LBP Variants for 3D Relief Patterns Classification / Claudio Tortorici, Naoufel Werghi, Stefano Berretti. - STAMPA. - (2017), pp. 1-12. (Intervento presentato al convegno VII International Workshop Representation, Analysis and Recognition of Shape and Motion from Image Data, RFMI 2017 tenutosi a Centre Paul-Langevin Aussois, Savoie, France nel December, 17-20, 2017).
Defining mesh-LBP Variants for 3D Relief Patterns Classification
Stefano Berretti
2017
Abstract
Extending the concept of texture to the geometry of a mesh manifold surface, opened the way to the idea of classifying 3D relief patterns as an emerging topic in 3D Computer Vision, with several potential applications. In this paper, we propose an original modelling solution to address this novel task. Following the recent introduction of the LBP computation framework on mesh manifolds(mesh-LBP),wefirstextendthisframeworktothedifferentvariantsof 2DLBPbydefiningmesh-LBPvariants.Thecomplianceoftheseextensionswith theoriginalLBPintermsofuniformityisalsoinvestigated.Then,weproposeda complete framework for relief patterns classification, which performs mesh preprocessing, multi-scale mesh-LBP extraction and descriptors classification. Experimental results on the SHREC’17 dataset showed competitive performance with respect to state of the art solutions.I documenti in FLORE sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.