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• W2954929956 abstract "Nowadays, big data of digital media (including images, videos and 3D graphical models) are frequently modeled as low-dimensional manifold meshes embedded in a high-dimensional feature space. In this paper, we summarized our recent work on geodesic centroidal Voronoi tessellations(GCVTs), which are intrinsic geometric structures on manifold meshes. We show that GCVT can find a widely range of interesting applications in computer vision and graphics, due to the efficiency of search, location and indexing inherent in these intrinsic geometric structures. Then we present the challenging issues of how to build the combinatorial structures of GCVTs and establish their time and space complexities, including both theoretical and algorithmic results." @default.
• W2954929956 created "2019-07-12" @default.
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• W2954929956 date "2019-07-01" @default.
• W2954929956 modified "2023-09-27" @default.
• W2954929956 title "Geodesic Centroidal Voronoi Tessellations: Theories, Algorithms and Applications." @default.
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