Inverse Renormalization Group Transformation in Bayesian Image Segmentations

01/05/2015 ∙ by Kazuyuki Tanaka, et al. ∙ Tohoku University 0

A new Bayesian image segmentation algorithm is proposed by combining a loopy belief propagation with an inverse real space renormalization group transformation to reduce the computational time. In results of our experiment, we observe that the proposed method can reduce the computational time to less than one-tenth of that taken by conventional Bayesian approaches.



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The authors are grateful to Prof. Federico Ricci-Tersenghi of the Department of Physics, University of Roma La Sapienza for valuable comments. This work was partly supported by the JST-CREST and the Grants-In-Aid (No.25280089) for Scientific Research from the Ministry of Education, Culture, Sports, Science and Technology of Japan.


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