Analysis Of Pattern Spectrum And Recognition Of Targets

How to extract invariable features of images and construct a efficient system of classification and recognition is always a hot spot in the field of computer vision.Mathematical morphology has already been widely used in all fields of image processing in recent years. Pattern Spectrum based on mathematical morphology describes not only dimension distributing of an image but also shape of this image. So we can make it a parameter of distinguishing different images.In this paper, we introduce several operations of mathematical morphology and their characters at first. Then we prove and analyze that if pattern spectrum has invariable features for translation rotation and zoom of images. Secondly we provide an improved normalized pattern spectrum based on the above analysis. Of course this normalized pattern spectrum has invariable features for translation rotation and zoom of images. It can describe shapes of images well and truly except for those images those have noise. In order to resist its sensitivity to noise, we adopt another pattern spectrum-high-level pattern spectrum that is defined based on alternating sequential morphological transformation. Experiments prove that high-level pattern spectrum not only has invariable features for translation rotation and zoom of images but alsocan resist the influence of noise on the shapes of images.In the aspect of classification and recognition of targets, we combine mathematical morphology with artificial neural network. Here we utilize a back propagation algorithm (BP). And then we use the normalized pattern spectrum and high-level pattern spectrum separately as the characters of recognition and the inputs of BP. By training this map, we can classify and recognize all images.Experimental results show that the method that combining mathematical morphology with artificial neural network is also a good way to realize the correct classification and recognition.