Texture recognition using a superfast cellular neural network VLSI chip in a real experimental environment

We have developed a new single-chip texture classifier smart-sensor system. Its main part is a programmable Cellular Neural Network (CNN) VLSI chip with optical input and an execution time of a few microseconds. This chip contains a dynamic and locally interconnected 2-D cell array. It executes a theoretically new method for texture classification, compared to the other convolution-based feature extraction methods, since here we have feedback convolution as well. Depending on the kernel-parameters, this array can execute filtering, moving, linear and nonlinear effects at the same time. The parameters of the feedback and feed-forward convolutions are optimized through a genetic learning using the 22*20 CNN chip itself. This chip has a simplified architecture with binary input/output, but it gives good recognition results: this CNN chip with a simple 3*3 kernel can reliably classify 5 natural Brodatz textures. Using more templates for decision-making, more textures can be separated and a classification-error of less than 1% has been achieved.