@inproceedings{555b824d9a1546f187fc7e365ddf20ec,
title = "Defining mesh-LBP variants for 3D relief patterns classification",
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), we first extend this framework to the different variants of 2D LBP by defining mesh-LBP variants. The compliance of these extensions with the original LBP in terms of uniformity is also investigated. Then, we proposed a complete framework for relief patterns classification, which performs mesh preprocessing, multi-scale mesh-LBP extraction and descriptors classification. Experimental results on the SHREC{\textquoteright}17 dataset showed competitive performance with respect to state of the art solutions.",
author = "Claudio Tortorici and Naoufel Werghi and Stefano Berretti",
note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2019.; 7th International Workshop on Representations, Analysis and Recognition of Shape and Motion from Imaging Data, RFMI 2017 ; Conference date: 17-12-2017 Through 20-12-2017",
year = "2019",
doi = "10.1007/978-3-030-19816-9_12",
language = "British English",
isbn = "9783030198152",
series = "Communications in Computer and Information Science",
publisher = "Springer Verlag",
pages = "151--166",
editor = "Liming Chen and Faouzi Ghorbel and {Ben Amor}, Boulbaba",
booktitle = "Representations, Analysis and Recognition of Shape and Motion from Imaging Data - 7th International Workshop, RFMI 2017, Revised Selected Papers",
address = "Germany",
}