Study Of Color Image Processing Using Unit-Linking PCNN

Human intuitive thinking is a distributed storage of information together, the fundamental point is that: 1. information is excited by neurons distributed in a pattern stored in the network; 2. information processing is through the interaction between neurons at the same time dynamic process to be completed. Mimic the human brain from the perspective of intelligence, a kind of structure closer to human intelligence information processing systems and designing a new computer processing model to resolve practical engineering and scientific research in the field of Von Neumann computer's difficult problems, which prompted the birth of artificial neural networks.Pulse Coupled Neural Network (PCNN) is different from the traditional has a biology background and a new generation of artificial neural network artificial neural network. PCNN neuron-specific use of pulse distribution, dual-channel multiplying the modulation, the threshold characteristics of the dynamic variable, a good simulation of biological neural fatigue, refractory period, pulse excitation phenomena, such PCNN model to the actual biological neural network a step closer, its ability to signal processing around more, in recent years by a large number of researchers attention. It can be applied to image processing, pattern recognition, optimization and other fields. In image processing, PCNN shows unique advantages, particularly image segmentation and edge detection, image processing has been a hot research.This model of the PCNN-depth study on the use Unit-Linking PCNN model for color image segmentation; the use of simplified model of PCNN segmentation of color images after the use of Unit-Linking PCNN model for the implementation of edge detection in color images. This paper addresses the key parameters of simplified model of PCNN difficult choice, color image segmentation and edge extraction quality issues.Research work and new contributions of the dissertation are as follows:First, the dissertation summarized the PCNN model and principle, normal improved model and principle, Unit-Linking PCNN model and principle.Second, a HSI color space based on color image segmentation. Color image is decomposed into color channels and brightness channel, the right color channel using Unit-Linking PCNN segmentation algorithm to segment, on the luminance channel using Otsu threshold segmentation algorithm, and then split the result after the final merger segmentation results. Experimental results show that the color components and luminance component split in different ways after the merger was a good result, the RGB space to solve the problem of high relevance.Third, a special color image pre-processing method. The spatial transformation of color images from RGB to HSV space, on the luminance component V a logarithmic transformation back to RGB space conversion, enhanced the contrast of the image object and background, to improve the image of the visual effects, the image is converted into a more suitable eye observation and analysis of identification in the form of machinery, from the image to get more useful information to enhance the final color image segmentation and edge detection results.Fourth, in the process of color image segmentation using genetic algorithm parameters of PCNN model automatically selects the key, to avoid the numerous parameters, set difficulty, the results of the parameters of sensitive issues. Fifth, use the skew calculation of indicators as evaluation criteria in image segmentation processing. Compared with the maximum entropy and cross entropy, the minimum skew index method and the method of Ostu computational considerably, but the maximum entropy and cross entropy so slowly as it relates to the number of logarithmic operation.Sixth, the color image edge detection, a pair of R, G, B values of the three components of the two split graph edge detection map corresponding to the merger strategy of the combined weighting methods, thereby reducing the number of broken edges.