Multigrid MRF based picture segmentation with cellular neural networks

Abstract

Due to the large computation power needed for Markovian Random Field (MRF) based image processing, new variations of basic MRF models are implemented. The Cellular Neural Network (CNN) architecture, implemented in real VLSI circuits, is of superior speed in image processing. This very fast CNN can implement the ideas of existing MRF models, which would result in real time processing of images. This VLSI solution gives new tasks since the CNN has a special local architecture. A type of MRF image segmentation with Modified Metropolis Dynamics (MMD) can be well implemented in the CNN architecture. In this paper, we address the improvement of the existing CNN method. We have tried out different multigrid models and compared segmentation results. The main reason for this research is to find the proper implementation of the CNN-MRF technique on CNNs according to the abilities of today''s and future''s VLSI CNN systems.