Name
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. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1073/pnas.0608811103 | PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA |
Keywords | Field | DocType |
computation, vision | Computer science,Algorithm,Probabilistic logic,Maximum a posteriori estimation,Generative grammar,Skeleton (computer programming),Bayes estimator,Computation,Bayesian probability,Bayes' theorem | Journal |
Volume | Issue | ISSN |
103 | 47 | 0027-8424 |
Citations | PageRank | References |
10 | 0.71 | 3 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jacob Feldman | 1 | 10 | 0.71 |
| Singh, Manish | 2 | 13 | 2.12 |