Metric-induced optimal embedding for intrinsic 3D shape analysis

Rongjie Lai*, Yonggang Shi, Kevin Scheibel, Scott Fears, Roger Woods, Arthur W. Toga, Tony Chan

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

26 Scopus citations

Abstract

For various 3D shape analysis tasks, the Laplace-Beltrami(LB) embedding has become increasingly popular as it enables the efficient comparison of shapes based on intrinsic geometry. One fundamental difficulty in using the LB embedding, however, is the ambiguity in the eigen-system, and it is conventionally only handled in a heuristic way. In this work, we propose a novel and intrinsic metric, the spectral l2-distance, to overcome this difficulty. We prove mathematically that this new distance satisfies the conditions of a rigorous metric. Using the resulting optimal embedding determined by the spectral l2-distance, we can perform both local and global shape analysis intrinsically in the embedding space. We demonstrate this by developing a template matching approach in the optimal embedding space to solve the challenging problem of identifying major sulci on vervet cortical surfaces. In our experiments, we validate the robustness of our method by the successful identification of major sulcal lines on a large data set of 698 cortical surfaces and illustrate its potential in brain mapping studies.

Original languageEnglish (US)
Title of host publication2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2010
Pages2871-2878
Number of pages8
DOIs
StatePublished - Aug 31 2010
Event2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2010 - San Francisco, CA, United States
Duration: Jun 13 2010Jun 18 2010

Publication series

NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
ISSN (Print)1063-6919

Other

Other2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2010
CountryUnited States
CitySan Francisco, CA
Period06/13/1006/18/10

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

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