mpLBP: An Extension of the Local Binary Pattern to Surfaces based on an Efficient Coding of the Point Neighbours

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dc.contributor.authorMoscoso Thompson, Eliaen_US
dc.contributor.authorBiasotti, Silviaen_US
dc.contributor.authorDigne, Julieen_US
dc.contributor.authorChaine, Raphaƫlleen_US
dc.contributor.editorBiasotti, Silvia and LavouƩ, Guillaume and Veltkamp, Remcoen_US
dc.date.accessioned2019-05-04T14:05:56Z
dc.date.available2019-05-04T14:05:56Z
dc.date.issued2019
dc.description.abstractThe description of surface textures in terms of repeated colorimetric and geometric local surface variations is a crucial task for several applications, such as object interpretation or style identification. Recently, methods based on extensions to the surface meshes of the Local Binary Pattern (LBP) or the Scale-Invariant Feature Transform (SIFT) descriptors have been proposed for geometric and colorimetric pattern retrieval and classification. With respect to the previous works, we consider a novel LBPbased descriptor based on the assignment of the point neighbours into sectors of equal area and a non-uniform, multiple ring sampling. Our method is able to deal with surfaces represented as point clouds. Experiments on different benchmarks confirm the competitiveness of the method within the existing literature, in terms of accuracy and computational complexity.en_US
dc.description.sectionheadersPaper Session 1
dc.description.seriesinformationEurographics Workshop on 3D Object Retrieval
dc.identifier.doi10.2312/3dor.20191056
dc.identifier.isbn978-3-03868-077-2
dc.identifier.issn1997-0471
dc.identifier.pages9-16
dc.identifier.urihttps://doi.org/10.2312/3dor.20191056
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/3dor20191056
dc.publisherThe Eurographics Associationen_US
dc.subjectInformation systems
dc.subjectInformation retrieval
dc.subjectComputing methodologies
dc.subjectShape modeling
dc.subjectShape analysis
dc.titlempLBP: An Extension of the Local Binary Pattern to Surfaces based on an Efficient Coding of the Point Neighboursen_US
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