Bayesian estimation of the shape skeleton
Bayesian estimation of the shape skeleton
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DOI：
10.1073/pnas.0608811103
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发表时间：
2006-11-21
影响因子：
11.1
通讯作者：
Singh, Manish
中科院分区：
文献类型：
--
作者：
Feldman, Jacob;Singh, Manish
自从Blum [Blum H（1973）J Theor Biol 38：205-287]引入形状的Skeletal表示以来，形状的Skeletal表示已经吸引了巨大的兴趣，因为它们有潜力提供紧凑但有意义的形状表示，适合于神经建模和计算应用。但是，形状骨架的有效计算仍然是一个臭名昭著的未解决的问题，现有的方法是非常敏感的噪声和简单的形状给出违反直觉的结果。在传统方法中，骨架由几何构造定义，并由确定性过程计算。我们介绍了贝叶斯概率方法，其中的形状被假定为有“成长”从骨架的随机生成过程。贝叶斯估计用于识别最有可能产生形状的骨架，即，最好的“解释”它，称为最大后验骨架。即使对于具有大量轮廓噪声的自然形状，该方法也提供了鲁棒的骨架表示，其分支对应于形状的自然部分。
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.