On spatio-temporal feature point detection for animated meshes

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 o...

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Bibliographic Details
Published inThe Visual computer Vol. 31; no. 11; pp. 1471 - 1486
Main Authors Mykhalchuk, Vasyl, Seo, Hyewon, Cordier, Frederic
Format Journal Article
LanguageEnglish
Published Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2015
Springer Nature B.V
Springer Verlag
Subjects
Algorithms
Animation
Approximation
Artificial Intelligence
Computer Graphics
Computer Science
Computer vision
Deformation
Differential equations
Dogs
Feature extraction
Finite element method
Image Processing and Computer Vision
Mathematical analysis
Neighborhoods
Operators (mathematics)
Original Article
Other Statistics
Pyramids
Sensors
Statistics
Animated mesh
Feature detection
Multi-scale representation
Difference of Gaussian
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Summary: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.
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ISSN:0178-2789
1432-2315
1432-8726
DOI:10.1007/s00371-014-1027-1