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Nonrigid Matching of Undersampled Shapes via Medial Diffusion
Author(s) -
Berger Matthew,
Silva Claudio T.
Publication year - 2012
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/j.1467-8659.2012.03164.x
Subject(s) - medial axis , diffusion process , diffusion , matching (statistics) , computer science , representation (politics) , process (computing) , invariant (physics) , construct (python library) , computer vision , geometry , artificial intelligence , mathematics , physics , knowledge management , statistics , innovation diffusion , operating system , programming language , politics , political science , law , mathematical physics , thermodynamics
We introduce medial diffusion for the matching of undersampled shapes undergoing a nonrigid deformation. We construct a diffusion process with respect to the medial axis of a shape, and use the quantity of heat diffusion as a measure which is both tolerant of missing data and approximately invariant to nonrigid deformations. A notable aspect of our approach is that we do not define the diffusion on the shape's medial axis, or similar medial representation. Instead, we construct the diffusion process directly on the shape. This permits the diffusion process to better capture surface features, such as varying spherical and cylindrical parts, as well as combine with other surface‐based diffusion processes. We show how to use medial diffusion to detect intrinsic symmetries, and for computing correspondences between pairs of shapes, wherein shapes contain substantial missing data.