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Image segmentation with one shape prior is an important problem in computer vision. Most algorithms not only share a similar energy definition, but also follow a similar optimization strategy. Therefore, they all suffer from the same drawbacks in practice such as slow convergence and difficult-to-tune parameters. In this paper, by reformulating the energy cost function, we establish an important connection...
In this paper, we show how the Metropolis-Hastings algorithm can be used to sample shapes from a distribution defined over the space of signed distance functions. We extend the basic random walk Metropolis-Hastings method to high-dimensional curves using a proposal distribution that can simultaneously maintain the signed distance function property and the ergodic requirement. We show that detailed...
We present a new variational level-set-based segmentation formulation that uses both shape and intensity prior information learned from a training set. By applying Bayes' rule to the segmentation problem, the cost function decomposes into shape and image energy parts. The shape energy is based on recently proposed nonparametric shape distributions, and we propose a new image energy model that incorporates...
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