MRF-MBNN: a novel neural network architecture for image processing

Authors:
Nian Cai;Jie Yang;Kuanghu Hu;Haitao Xiong
Affiliations:
Institute of Image Processing and Pattern Recognition, Shanghai Jiaotong University, Shanghai, China;Institute of Image Processing and Pattern Recognition, Shanghai Jiaotong University, Shanghai, China;Institute of Biophysics, Chinese Academy of Sciences, Chaoyang District, Beijing, China;Institute of Biophysics, Chinese Academy of Sciences, Chaoyang District, Beijing, China
Venue:
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
Year:
2005

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Abstract

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.