3DOR 15

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https://diglib.eg.org/handle/10.2312/12281

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Browsing 3DOR 15 by Subject "Curve"

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    Computing Local Binary Patterns on Mesh Manifolds for 3D Texture Retrieval
    (The Eurographics Association, 2015) Werghi, Naoufel; Tortorici, Claudio; Berretti, Stefano; Bimbo, Alberto Del; I. Pratikakis and M. Spagnuolo and T. Theoharis and L. Van Gool and R. Veltkamp
    In this paper, we present and experiment a novel approach for retrieving 3D geometric texture patterns on 2D mesh-manifolds (i.e., surfaces in the 3D space) using local binary patterns (LBP) constructed on the mesh. The method is based on the recently proposed mesh-LBP framework [WBD15]. Compared to its depth-image counterpart, the mesh-LBP is distinguished by the following features: a) inherits the intrinsic advantages of mesh surface (e.g., preservation of the full geometry); b) does not require normalization; c) can accommodate partial matching. Experiments conducted with public 3D models with geometric texture showcase the superiority of the mesh-LBP descriptors in comparison with competitive methods.
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    A Spatio-Temporal Descriptor for Dynamic 3D Facial Expression Retrieval and Recognition
    (The Eurographics Association, 2015) Danelakis, Antonios; Theoharis, Theoharis; Pratikakis, Ioannis; I. Pratikakis and M. Spagnuolo and T. Theoharis and L. Van Gool and R. Veltkamp
    The recent availability of dynamic 3D facial scans has spawned research activity in recognition based on such data. However, the problem of facial expression retrieval based on dynamic 3D facial data has hardly been addressed and is the subject of this paper. A novel descriptor is created, capturing the spatio-temporal deformation of the 3D facial mesh sequence. Experiments have been implemented using the standard BU - 4DFE dataset. The obtained retrieval results exceed the state-of-the-art results and the new descriptor is much more frugal in terms of space requirements. Furthermore, a methodology which exploits the retrieval results, in order to achieve unsupervised dynamic 3D facial expression recognition is presented, in order to directly compare the proposed descriptor against the wealth of works in recognition. The aforementioned unsupervised methodology outperforms the supervised dynamic 3D facial expression recognition state-of-the-art techniques in terms of classification accuracy.