Cellular neural network (CNN) circuit design for modeling of early-stage human visual system

This paper proposes a novel CNN-based biological visual processing for hybrid-order texture boundary detection. The texture boundary detection is based on the first- and second-order features to model pre-attentive stage of human visual system. This system is implemented by using a parallel computing neural network, called cellular neural networks (CNN). This CNN design adopts the multi-layer architecture involving a 5/spl times/5 large neighborhood and is extended to be the 16/spl times/16 array size for image processing. The proposed circuit models have been verified and the proposed method can successfully detect the texture boundary in an image.