Texture segmentation by the 64/spl times/64 CNN chip

Abstract

CNN's fast image processing technology helps us to run high-speed filtering tasks for image enhancement, recognition or segmentation. Texture analysis is a specific task, since the whole image is processed massively parallel while we have a limited number of texture-specific filtering and evaluation steps. Former results of simulations and recognition results of simple CNN chips show that the CNN is an appropriate tool for this image-processing task. Now we see what the gray-scale image processor CNN chip at its limited memory capability and data-handling/-processing accuracy can complete for multi-texture images. We demonstrate and compare some of our earlier CNN-related texture analysis methods. Some methods to improve CNN configuration are proposed.