Titlebook: Computer Vision – ECCV 2012; 12th European Confer Andrew Fitzgibbon,Svetlana Lazebnik,Cordelia Schmi Conference proceedings 2012 Springer-V

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https://doi.org/10.1057/9780230373334y on minimizing objective functions to enforce color homogeneity. The optimization is accomplished by sophisticated methods that progressively build the superpixels, typically by adding cuts or growing superpixels. As a result, they are computationally too expensive for real-time applications. We in

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recession

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Emergence of the Modern Framework, 1850–1913class labels. We learn a model that can classify unseen test videos, as well as localize a region of interest in the video that captures the discriminative essence of the action class. A novel Similarity Constrained Latent Support Vector Machine model is developed to operationalize this goal. This m

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Offbeat

G. A. Fletcher B.A., M.Sc. (Econ.) challenging Hollywood2 benchmark [1,2,3]. These algorithms are based on local spatiotemporal descriptors that can be extracted either sparsely (at interest points) or densely (on regular grids), with dense sampling typically leading to the best performance [1]. Here, we investigate the benefit of s

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The Mechanism of Monetary Controlrt denoising methods are visually clearly distinguishable and possess complementary strengths and failure modes. Motivated by this observation, we introduce a powerful non-parametric image restoration framework based on Regression Tree Fields (RTF). Our restoration model is a densely-connected tract

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https://doi.org/10.1057/9780230372054 approach is not based on a sampling strategy, instead we combine the polynomial chaos expansion and stochastic finite elements with PDE-based image processing operators. With our approach and a moderate number of parameters in the models the full sensitivity analysis is obtained at the cost of a fe