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Computer Vision and Image Understanding
Nonlinear manifold learning for dynamic shape and dynamic appearance
Computer Vision and Image Understanding
Texture enhanced appearance models
Computer Vision and Image Understanding
Dynamic quantization for belief propagation in sparse spaces
Computer Vision and Image Understanding
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Interpretation of complex scenes using dynamic tree-structure Bayesian networks
Computer Vision and Image Understanding
Large deformation diffeomorphisms with application to optic flow
Computer Vision and Image Understanding
Optical flow based super-resolution: A probabilistic approach
Computer Vision and Image Understanding
Smart particle filtering for high-dimensional tracking
Computer Vision and Image Understanding
Contour tracking based on marginalized likelihood ratios
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Interpretation of complex scenes using dynamic tree-structure Bayesian networks
Computer Vision and Image Understanding
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Generative and discriminative models are best defined by the structure of their graphical representation. This paper introduces such a definition and uses it to argue that, in some practical cases, generative models need to be formulated in order to be implemented within generate-and-test algorithms. This argument is inspired mainly by the ideas of the late Donald MacKay and by considerations of computational complexity.