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