Work

Inverse Renormalization Group Transformation in Bayesian Image Segmentations

Year: 2015

Type: article

Abstract: 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.

Source: Journal of the Physical Society of Japan

DOI: 10.7566/jpsj.84.045001

Authors: Kazuyuki Tanaka, Shun Kataoka, Muneki Yasuda, Masayuki Ohzeki

Institutions: Tohoku University, Yamagata University, Kyoto University

Language: English

Topic: Medical Image Segmentation Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: Japan Society for the Promotion of Science

Awards: Statistical mechanics and Information processing for optimization and control by nonequilibrium relation, Schematization of probabilistic social network theory, 時間変化を伴う統計的計算モデルによるソーシャルネットワークの統計的計算理論の構築, Design Theory of Probabilistic Computational Models for Community Detections based on Non-Additive Volume and Entropy