Abstract

Constraint satisfaction neural networks (CSNNs) are proposed for image recognition which is the process to identify objects of interest in an image. The image recognition problem is formulated as a constraint satisfaction problem (CSP). The regions resulting from a signal-based image segmentation algorithm can be considered as objects and their entities as labels in the CSP. The proposed neural network is multi-layered and the function of each layer is to identify one physical entity (label) from the input regions (objects). Through cascading these layers, each label can be assigned to the corresponding region(s). Attention is focused on recognizing X-ray CT (computed tomography) images of human heads (e.g. anatomic structure in a medical image). The proposed CSNN is capable of identifying the components of a brain in a preliminarily segmented image. The major components in a human head include bone, ventricle and gyrus. Therefore, three layers are needed for this task. Each layer is constructed based on the principles proposed in Kohonen's self-organizing feature maps and the optimal linear associative memories and their weights are trained by the features of each entity in the prestored model. The experimental results show that the proposed approach is very promising.

Author Keywords: Constraint satisfaction problems; Image understanding; Self-organizing feature maps; Optimal linear associative memories; X-ray CT images

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