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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 \u201cgrown\u201d 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 \u201cexplains\u201d it, called the maximum\n                    <jats:italic>a posteriori<\/jats:italic>\n                    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.\n                  <\/jats:p>","DOI":"10.1073\/pnas.0608811103","type":"journal-article","created":{"date-parts":[[2006,11,13]],"date-time":"2006-11-13T20:38:56Z","timestamp":1163450336000},"page":"18014-18019","update-policy":"https:\/\/doi.org\/10.1073\/pnas.cm10313","source":"Crossref","is-referenced-by-count":137,"title":["Bayesian estimation of the shape skeleton"],"prefix":"10.1073","volume":"103","author":[{"given":"Jacob","family":"Feldman","sequence":"first","affiliation":[{"name":"Department of Psychology, Center for Cognitive Science, Rutgers, The State University of New Jersey, Piscataway, NJ 08854"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Manish","family":"Singh","sequence":"additional","affiliation":[{"name":"Department of Psychology, Center for Cognitive Science, Rutgers, The 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