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We present a novel optimization scheme in the popular active shape model(ASM) framework, which increases the accuracy and robustness of searching for a hypothesis shape. The determininistic fitting scheme in traditional ASM is substituted by a probabilistic estimation approach in our work. A set of weighted particles is used to represent each salient feature point to form a shape density, the particle...
Many vision problems can be cast as optimizing the conditional probability density function p(C\I) where I is an image and C is a vector of model parameters describing the image. Ideally, the density function p(C\I) would be smooth and unimodal allowing local optimization techniques, such as gradient descent or simplex, to converge to an optimal solution quickly, while preserving significant nonlinearities...
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