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- W193577214 abstract "Traditional scenario of probabilistic modelling is directed at generating samples having a given probability distribution. We argue that this scenario is impracticable for image modelling and should be replaced by more reasonable alternative one. This latter tends to generate samples having probabilities in the vicinity of the probability of a given training sample. Gibbs image models with multiple pairwise pixel interactions allow to explicitly implement the alternative scenario. 1 The University of Auckland, Computer Science Department, CITR, Tamaki Campus (Building 731), Glen Innes, Auckland, New Zealand On Comparing Two Scenarios for Probabilistic Image Modelling Georgy Gimel'farb CITR, Department of Computer Science, Tamaki Campus, The University of Auckland Private Bag 92019, Auckland 1, New Zealand Abstract: Traditional scenario of probabilistic modelling is directed at generating samples having a given probability distribution. We argue that this scenario is impracticable for image modelling and should be replaced by more reasonable alternative one. This latter tends to generate samples having probabilities in the vicinity of the probability of a given training sample. Gibbs image models with multiple pairwise pixel interactions allow to explicitly implement the alternative scenario." @default.
- W193577214 created "2016-06-24" @default.
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- W193577214 date "1998-01-01" @default.
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- W193577214 title "On Comparing Two Scenarios for Probabilistic Image Modelling." @default.
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