A Region-based Coupled MRF Model for Coarse Image Region Segmentation toward its VLSI Implementation

DOI Web Site Web Site 1 Citations 34 References
  • Liang Haichao
    Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology
  • Kawashima Yusuke
    Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology
  • Matsuzaka Kenji
    Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology
  • Nakada Kazuki
    Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology
  • Okada Masato
    Graduate School of Frontier Sciences, The University of Tokyo
  • Morie Takashi
    Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology

Bibliographic Information

Other Title
  • 集積回路化を目指した大局的画像領域分割のための領域ベース結合ＭＲＦモデル
  • シュウセキ カイロカ オ メザシタ タイキョクテキ ガゾウ リョウイキ ブンカツ ノ タメ ノ リョウイキ ベース ケツゴウ MRF モデル

Search this article

Abstract

Coupled Markov random field (MRF) models have been proposed so far for visual image processing. These are classified into boundary- and region-based models, where hidden variables that determine the interaction between the units corresponding to image pixels are given by line and label processes, respectively. In this paper, we have investigated a region-based coupled MRF model with phase variables as the hidden variables, and have modified the model for applications to coarse image-region segmentation by replacing some nonlinear functions in the update equations of the intensity and label processes with piecewise linear (PWL) functions. Using PWL functions facilitates VLSI implementation of coupled MRF models, and also makes their performance improved. We have verified that the modified region-based MRF model is superior to the resistive-fuse network, which is one of the boundary-based MRF models. We also propose an improvement of the modified region-based MRF model by introducing a new parameter, and evaluate the model performance in coarse image region segmentation.

Journal

Citations (1)*help

See more

References(34)*help

See more

Keywords

Details 詳細情報について

Report a problem

Back to top