3DOR 18

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https://diglib.eg.org/handle/10.2312/2632165

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Browsing 3DOR 18 by Subject "3D imaging"

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    Performing Image-like Convolution on Triangular Meshes
    (The Eurographics Association, 2018) Tortorici, Claudio; Werghi, Naoufel; Berretti, Stefano; Telea, Alex and Theoharis, Theoharis and Veltkamp, Remco
    Image convolution with a filtering mask is at the base of several image analysis operations. This is motivated by Mathematical foundations and by the straightforward way the discrete convolution can be computed on a grid-like domain. Extending the convolution operation to the mesh manifold support is a challenging task due to the irregular structure of the mesh connections. In this paper, we propose a computational framework that allows convolutional operations on the mesh. This relies on the idea of ordering the facets of the mesh so that a shift-like operation can be derived. Experiments have been performed with several filter masks (Sobel, Gabor, etc.) showing state-of-the-art results in 3D relief patterns retrieval on the SHREC'17 dataset. We also provide evidence that the proposed framework can enable convolution and pooling-like operations as can be needed for extending Convolutional Neural Networks to 3D meshes.
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    Person Re-Identification from Depth Cameras using Skeleton and 3D Face Data
    (The Eurographics Association, 2018) Pala, Pietro; Seidenari, Lorenzo; Berretti, Stefano; Bimbo, Alberto Del; Telea, Alex and Theoharis, Theoharis and Veltkamp, Remco
    In the typical approach, person re-identification is performed using appearance in 2D still images or videos, thus invalidating any application in which a person may change dress across subsequent acquisitions. For example, this is a relevant scenario for home patient monitoring. Depth cameras enable person re-identification exploiting 3D information that captures biometric cues such as face and characteristic dimensions of the body. Unfortunately, face and skeleton quality is not always enough to grant a correct recognition from depth data. Both features are affected by the pose of the subject and the distance from the camera. In this paper, we propose a model to incorporate a robust skeleton representation with a highly discriminative face feature, weighting samples by their quality. Our method combining face and skeleton data improves rank-1 accuracy compared to individual cues especially on short realistic sequences.