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MRF-MBNN: A novel neural network architecture for image processing. (English) Zbl 1084.68555

Wang, Jun (ed.) et al., Advances in neural networks – ISNN 2005. Second international symposium on neural networks, Chongqing, China, May 30 – June 1, 2005. Proceedings, Part II. Berlin: Springer (ISBN 3-540-25913-9/pbk). Lecture Notes in Computer Science 3497, 673-678 (2005).
Summary: Contextual information and a priori knowledge play important roles in image segmentation based on neural networks. This paper proposed a method for including contextual information in a model-based neural network (MBNN) that has the advantage of combining a priori knowledge. This is achieved by including Markov random field (MRF) into the MBNN and this novel neural network is termed as MRF-MBNN. Then the proposed method is applied to segmenting the images. Experimental results indicate the MRF-MBNN is superior to the MBNN in image segmentation. This study is a successful attempt of incorporating contextual information and a prior knowledge into neural networks to segment images.
For the entire collection see [Zbl 1073.68014].

MSC:

68T05 Learning and adaptive systems in artificial intelligence
92B20 Neural networks for/in biological studies, artificial life and related topics
68U10 Computing methodologies for image processing

Software:

MRF-MBNN
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