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"Expressing Bayesian Fusion as a Product of Distributions: Application to Randomized Hough Transform"^^ .
"Data fusion is a common issue of mobile robotics, computer assisted\nmedical diagnosis or behavioral control of simulated character for instance. However\ndata sources are often noisy, opinion for experts are not known with absolute\nprecision, and motor commands do not act in the same exact manner on the environment.\nIn these cases, classic logic fails to manage efficiently the fusion process.\nConfronting different knowledge in an uncertain environment can therefore be adequately\nformalized in the bayesian framework.\nBesides, bayesian fusion can be expensive in terms of memory usage and processing\ntime. This paper precisely aims at expressing any bayesian fusion process as a\nproduct of probability distributions in order to reduce its complexity. We first study\nboth direct and inverse fusion schemes. We show that contrary to direct models,\ninverse local models need a specific prior in order to allow the fusion to be computed\nas a product. We therefore propose to add a consistency variable to each local\nmodel and we show that these additional variables allow the use of a product of the\nlocal distributions in order to compute the global probability distribution over the\nfused variable. Finally, we take the example of the Randomized Hough Transform.\nWe rewrite it in the bayesian framework, considering that it is a fusion process\nto extract lines from couples of dots in a picture. As expected, we can find back\nthe expression of the Randomized Hough Transform from the literature with the\nappropriate assumptions."^^ .
"2003" .
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"Pradalier"^^ .
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"Expressing Bayesian Fusion as a Product of Distributions: Application to Randomized Hough Transform (PDF)"^^ .
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"Pradalier03a.pdf"^^ .
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"Expressing Bayesian Fusion as a Product of Distributions: Application to Randomized Hough Transform (Image (PNG))"^^ .
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"Expressing Bayesian Fusion as a Product of Distributions: Application to Randomized Hough Transform (Indexer Terms)"^^ .
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"HTML Summary of #3758 \n\nExpressing Bayesian Fusion as a Product of Distributions: Application to Randomized Hough Transform\n\n" .
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