Studies on biology basis from human being or other animals have attracted an ever increasing attention in pattern recognition. This paper describe a new model of pattern recognition principles, witch is based on “matter cognition” instead of “matter classification” in traditional statistical pattern recognition. This new model is better closer to the function of human being, rather than traditional statistical pattern recognition using “optimal separating” as its main principle. So the new model of pattern recognition is called the Bionic（or cognitive） Pattern Recognition. In the support of this theory, the pulse coupled neural network (PCNN), an entirely different neural network from traditional artificial ones, is used for image target recognition. Through the contrast of the image, the linking strength of each pixel can be chosen adaptively. After the processing of PCNN with the adaptive linking strength, new fire mapping images are obtained for each image from sensor. The clear objects of each original image are decided by the compare-selection operator with the fire mapping images pixel by pixel and then all of them are merged into a new clear image. As a result, the target which was polluted by noise was recognized correctly.
Statistical Pattern Recognition Problems and the Multiple Classes Random Neural Network Model
