Inverse Renormalization Group Transformation in Bayesian Image Segmentations

Inverse Renormalization Group Transformation in Bayesian Image Segmentations

Image Segmentations Kazuyuki Tanaka ∗1, Shun Kataoka, Muneki Yasuda, Masayuki Ohzeki Graduate School of Information Sciences, Tohoku University, 6-3-09 Aramaki-aza-aoba, Aoba-ku, Sendai 980-8579, Japan Graduate School of Science and Engineering, Yamagata University, 4-3-16 Jyounan, Yonezawa 992-8510, Japan Graduate School of Informatics, Kyoto University, 36-1 Yoshida-Honmachi, Sakyo-ku, Kyoto ...

Momentum-Space Renormalization Group Transformation in Bayesian Image Modeling by Gaussian Graphical Model

Probabilistic graphical models based on Bayesian statistics are powerful tools for carrying out statistical inferences Probabilistic graphical models are regarded as an application of classical spin systems from statistical-mechanical viewpoint . Moreover, these provide many useful applications, not only for image processing but also for other inference systems in high-dimensional data driven s...

Bayesian image segmentations by Potts prior and loopy belief propagation

propagation Kazuyuki Tanaka ∗1, Shun Kataoka, Muneki Yasuda, Yuji Waizumi and Chiou-Ting Hsu Graduate School of Information Sciences, Tohoku University, 6-3-09 Aramaki-aza-aoba, Aoba-ku, Sendai 980-8579, Japan Graduate School of Science and Engineering, Yamagata University, 4-3-16 Jyounan, Yonezawa 992-8510, Japan Department of Computer Science, National Tsing Hua University, No.101, Section 2,...

Renormalization Group Renormalization Group

Renormalization Group Transformation for Hamiltonian Dynamical Systems in Biological Networks

We apply the renormalization group theory to the dynamical systems with the simplest example of basic biological motifs. This includes the interpretation of complex networks as the perturbation to simple network. This is the first step to build our original framework to infer the properties of biological networks, and the basis work to see its effectiveness to actual complex systems.