Study And Application Of Vision Perception Computation Models

Computer vision is a challenging field with rich contents, and there is an abundance of research achievement about it now. As further research in this field, the difficulties and new challenges in the field also come out. Basically, the difficulty is due to the limitation of the understanding of human's visual mechanism and the technology restriction to vision computing. Nowadays, many scientists hope to relate their work with biophysics and neurophysiology. From a long-term point of view, the eternal purpose of studying the computer vision is to form human's visual computing theory and then to establish the commonly visual system which can be compared to human's visual system. Under the idear of bionics, this paper is to provide the biological enlightenment for the computer vision and form the applied model in order to meet the need of processing pictures by summarizing the latest study of the biological vision. The paper contributes to the following creative work in the following aspects:1. Based on further study in the Pulse Coupled Neural Network (PCNN), the rule of parameters determination is proposed. PCNN is made up of nonlinear dynamic neurons with variable threshold, and it has many unique advantages such as spatio-temporal and coupled oscillation. Because of the characters of PCNN, the study of PCNN has not only great theoretical significance but also the wide application prospect. The paper analyzes the processing mechanism of PCNN and probe into the influences on the model behavior caused by the parameters. And it also provides the parameters standard by theoretically deducing and computer simulation, which solve the problem of parameter determination when using PCNN in technology application.2. An improved model named ADPCNN is proposed by combining the Anisotropic Diffusion (AD) which is based on the partial differential equations and the typical PCNN model. It is difficult to make sure of the modifying strategy of pixel gray values in image de-noising by using the traditional PCNN because it only uses the nonlinear projection of pixel gray values and the periodic oscillation of nerve cell. But the way of collecting the information about the surrounding pixels provide the strategy instruction for modifying the pixel gray values. So ADPCNN model solves the problem of modifying the pixel gray values which affects the typical PCNN in image smoothing.3. According to the non-classical receptive field of biology vision nerve cell, the paper presents the arithmetic operators of Orientation Difference of Gaussians (ODOG) with the help of the present theories of computer vision. ODOG operators can adapt the requirement of the different sizes by changing the area size of the center and surrounding of receptive field. Also it has the characteristic of orientation and is answer for the new development of non-classical receptive field. Compared to the traditional image processing, it has great advantages in the field of image contour extraction.4. Present the improving model named SAMPCNN by combing the PCNN and ODOG. The traditional PCNN model, if only by its characteristic of coupled oscillation, is not ideally in image segmentation. But the ODOG based on the vision selective attention mechanism can effectively collect the interesting information and then segment the image by the coupled oscillation of the PCNN.