Titlebook: Energy Minimization Methods in Computer Vision and Pattern Recognition; Third International Mário Figueiredo,Josiane Zerubia,Anil K. Jain

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書(shū)目名稱Energy Minimization Methods in Computer Vision and Pattern Recognition影響因子(影響力)




書(shū)目名稱Energy Minimization Methods in Computer Vision and Pattern Recognition影響因子(影響力)學(xué)科排名




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書(shū)目名稱Energy Minimization Methods in Computer Vision and Pattern Recognition被引頻次




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書(shū)目名稱Energy Minimization Methods in Computer Vision and Pattern Recognition讀者反饋




書(shū)目名稱Energy Minimization Methods in Computer Vision and Pattern Recognition讀者反饋學(xué)科排名

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Definitions and Clinical Presentation,given functional subject to some noise constraints. A MAP estimator which uses a Markov or a maximum entropy random field model for a prior distribution can be viewed as a minimizer of a variational problem. Using notions from robust statistics, a variational filter called . is proposed. It yields t

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https://doi.org/10.1007/978-3-030-33691-2. coming from a real distance) is also very important to allow fast retrieval on large databases. Moreover, these similarity functions should be flexible enough to be tuned to fit users behaviour. These two constraints, . and . are generally difficult to fulfill. Our contribution is two folds: We sh

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R. Lorenz,H. Kanaya,R. D. Nagpal,J. Iizukaocedures if Bayesian principles are used. Heuristic approaches have been used instead in practical applications..This paper tries to overcome this difficulty by proposing an algorithm which is derived from sound theoretical principles and fast. This algorithm is based on the expansion of the noise p

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Spontaneous Order and the Utopian Collectivee is finding the optimal structure of the model, namely, its number of states. Although a lot of work has been carried out in the context of the model selection, few work address this specific problem, and heuristics rules are often used to define the model depending on the tackled application. In t

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