MRF-MBNN: A Novel Neural Network Architecture for Image Processing

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.

• Type: Conference
• Year: 2005
• Where: ISNN
• Authors: Nian Cai, Jie Yang, Kuanghu Hu, Haitao Xiong
• Keywords: Contextual Information, ISNN 2005, Neural Networks, Priori Knowledge