Coarse Image Region Segmentation in Spatio-Temporal Domain Using a Region-based Coupled MRF Model with Phase Dynamics
Kazuki Nakada, Kenji Matsuzaka, Takashi Morie · International Conference on Intelligent Information Processing · 2010
Towards hardware implementation of real-time visual image processing, we propose a region- based coupled Markov Random Field (MRF) model with phases as hidden variables for coarse image region segmentation tasks. In general, two types of coupled MRF models, boundary-based and region-based, are known according to their hidden variables that play a crucial role for detecting discontinuities in intensity, color, depth and motion in image scenes. A region-based coupled MRF model with phases as hidden variables has been proposed for image restoration task. For coarse region image segmentation tasks, we customize the previous model in view of efficient hardware implementation. Our model has an advantage over the resistive-fuse network, which is a boundary-based coupled MRF model, in dealing with the hidden variables explicitly. Consequently, our model can extract closed regions from given images by phases as labels at different timing and act as a spatial-temporal nonlinear filter.