Abstract
We present a unified Bayesian approach to shape representation and related problems in perceptual organization, including part decomposition, shape similarity, figure/ground estimation, and 3D shape. The approach is based on the idea of estimating the skeletal structure most likely to have generated the observed shape via a process of stochastic “growth.” We survey the approach briefly and show how it can be extended in a principled way to solve a wide array of related problems.
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Feldman, J., Singh, M., Briscoe, E., Froyen, V., Kim, S., Wilder, J. (2013). An Integrated Bayesian Approach to Shape Representation and Perceptual Organization. In: Dickinson, S., Pizlo, Z. (eds) Shape Perception in Human and Computer Vision. Advances in Computer Vision and Pattern Recognition. Springer, London. https://doi.org/10.1007/978-1-4471-5195-1_4
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DOI: https://doi.org/10.1007/978-1-4471-5195-1_4
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