Skeletal representations of shape have attracted enormous interest ever since their introduction by Blum [Blum H (1973) J Theor Biol 38:205-287], because of their potential to provide a compact, but meaningful, shape representation, suitable for both neural modeling and computational applications. But effective computation of the shape skeleton remains a notorious unsolved problem; existing approaches are extremely sensitive to noise and give counterintuitive results with simple shapes. In conventional approaches, the skeleton is defined by a geometric construction and computed by a deterministic procedure. We introduce a Bayesian probabilistic approach, in which a shape is assumed to have "grown" from a skeleton by a stochastic generative process. Bayesian estimation is used to identify the skeleton most likely to have produced the shape, i.e., that best "explains" it, called the maximum a posteriori skeleton. Even with natural shapes with substantial contour noise, this approach provides a robust skeletal representation whose branches correspond to the natural parts of the shape.
Bayesian estimation of the shape skeleton. Publishing Authors By Initials
Bayesian estimation of the shape skeleton. Journal Published:
PUBLICATION TYPE: Research Support, U.S. Gov't,
Journal: Proceedings of the National Academy of Sciences of
VOLUME: 103
Page Numbers: 18014-9
Journal Abbreviation: Proc. Natl. Acad. Sci. U.S.A.
ISSN: 0027-8424
DAY: 13
MONTH: 11
YEAR: 2006
Bayesian estimation of the shape skeleton. Information
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LANGUAGE: eng
NlmUniqueID: 7505876
Bayesian estimation of the shape skeleton. Keywords Mesh Terms:
KEYWORDS: Skeleton
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Grant and Affiliation Information for Bayesian estimation of the shape skeleton.
AFFILIATION: Department of Psychology, Center for Cognitive Science, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA. jacob@ruccs.rutgers.edu
Country: United States
AGENCY: United States NEI
GRANT: R01 EY15888
ACRONYM: EY
MEDLINETA: Proc Natl Acad Sci U S A
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