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• W2030690158 startingPage "18014" @default.
• W2030690158 abstract "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." @default.
• W2030690158 created "2016-06-24" @default.
• W2030690158 creator A5002009029 @default.
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• W2030690158 date "2006-11-21" @default.
• W2030690158 modified "2023-10-11" @default.
• W2030690158 title "Bayesian estimation of the shape skeleton" @default.
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• W2030690158 doi "https://doi.org/10.1073/pnas.0608811103" @default.
• W2030690158 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/1635976" @default.
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