caa a22 4500 605541434 CHVBK 20210128100916.0 cr unu---uuuuu 210128e20151101xx s 000 0 eng 10.1007/s00371-014-1027-1 doi (NATIONALLICENCE)springer-10.1007/s00371-014-1027-1 On spatio-temporal feature point detection for animated meshes [Elektronische Daten] [Vasyl Mykhalchuk, Hyewon Seo, Frederic Cordier] Although automatic feature detection has been a long-sought subject by researchers in computer graphics and computer vision, feature extraction on deforming models remains a relatively unexplored area. In this paper, we develop a new method for automatic detection of spatio-temporal feature points on animated meshes. Our algorithm consists of three main parts. We first define local deformation characteristics, based on strain and curvature values computed for each point at each frame. Next, we construct multi-resolution space-time Gaussians and difference-of-Gaussian (DoG) pyramids on the deformation characteristics representing the input animated mesh, where each level contains 3D smoothed and subsampled representation of the previous level. Finally, we estimate locations and scales of spatio-temporal feature points by using a scale-normalized differential operator. A new, precise approximation of spatio-temporal scale-normalized Laplacian has been introduced, based on the space-time DoG. We have experimentally verified our algorithm on a number of examples and conclude that our technique allows to detect spatio and temporal feature points in a reliable manner. Springer-Verlag Berlin Heidelberg, 2014 Feature detection nationallicence Animated mesh nationallicence Multi-scale representation nationallicence Difference of Gaussian nationallicence Mykhalchuk Vasyl University of Strasbourg, Strasbourg, France aut Seo Hyewon University of Strasbourg, Strasbourg, France aut Cordier Frederic University of Haute Alsace, Mulhouse, France aut The Visual Computer Springer Berlin Heidelberg 31/11(2015-11-01), 1471-1486 0178-2789 31:11<1471 2015 31 371 https://doi.org/10.1007/s00371-014-1027-1 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00371-014-1027-1 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Mykhalchuk Vasyl University of Strasbourg, Strasbourg, France aut NATIONALLICENCE 700 1- Seo Hyewon University of Strasbourg, Strasbourg, France aut NATIONALLICENCE 700 1- Cordier Frederic University of Haute Alsace, Mulhouse, France aut NATIONALLICENCE 773 0- The Visual Computer Springer Berlin Heidelberg 31/11(2015-11-01), 1471-1486 0178-2789 31:11<1471 2015 31 371