Abstract
Due to the large computation power needed in image processing methods based on Markovian Random Field (MRF) [6], new variations of basic MRF models are implemented. The Cellular Neural Network [5,14,15] (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. On the other hand this VLSI solution gives new tasks since the CNN has a special local architecture [4], but it is already shown that a type of MRF image segmentation with Modified Metropolis Dynamics (MMD [9]) can be well implemented in the CNN architecture [18]. In this paper, we address the improvement of the existing CNN method [17]. We have tested different multigrid models and compared segmentation results. The main reason for this research is to find proper implementation of the CNN-MRF technique on CNNs taking into consideration the abilities of today's and future's VLSI CNN systems.
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© 1997 Springer-Verlag Berlin Heidelberg
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Czúni, L., Szirányi, T., Zerubia, J. (1997). Multigrid MRF based picture segmentation with cellular neural networks. In: Sommer, G., Daniilidis, K., Pauli, J. (eds) Computer Analysis of Images and Patterns. CAIP 1997. Lecture Notes in Computer Science, vol 1296. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-63460-6_136
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DOI: https://doi.org/10.1007/3-540-63460-6_136
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