Sub-pattern texture recognition using intelligent focal-plane imaging sensor of small window-size
In this paper we demonstrate how to use statistical evaluation for texture recognition in the case of window-size of the imaging focal-plane sensor being smaller than the pattern of the texture. The evaluation method is similar to the sub-pixel pattern recognition developed by the first author. We have reported in an earlier publication on the development of a new single-chip texture classifier smart-sensor system, whose main part is a cellular nonlinear network (CNN) VLSI chip. This architecture is very fast but it has a limited window-size. Now we show that this architecture can effectively recognize textures of periodicity larger than the window-size. As a result, we recognized 15 Brodatz-textures by using a 2022 CNN chip with a 0.4% error-rate.
cellular nonlinear network smart sensors genetic algorithm texture analysis sub-pixel recognition density estimation