The Study Of Mage Segmentation Based On Particle Swarm Optimization Algorithm And PCNN

In recent years, with the rapid development of computer simulation technology and digital multimedia technology, digital image processing technology has been paid more and more attention. In the field of image processing, how to segment the target of interest is the most basic content. But due to the nature of the images differ in thousands of ways, especially in the case of difficult to distinguish between target and background. Make accurately and efficiently extract the target which we are interested in become relatively difficult, so the research work in this area that people spend a lot of energy.PCNN model for image segmentation, do not need to set conditions, only depends on nature of the image, and can be different levels of image segmentation, so the application is very extensive in the field of image segmentation. But because of its parameter settings and selection more difficult, while setting different parameters tend to make the results vary widely, thereby limiting its further development. The use of intelligent algorithm for fast optimization ability is one of the effective ways to solve these problems, such as ant colony algorithm, genetic algorithm. In this paper, the current popular PSO algorithm was adopted to realize the optimization of parameters, the characteristics of the algorithm is easy to understand, simple to set up and high efficiency, and easy to combine with other algorithms. Through the two-dimensional Gaussian function extremum optimization, the submission of master’s thesis achieved ideal results, and verified its validity and feasibility.In order to solve the PCNN model have long relied on artificial and experience value selection of parameters difficult situation, we propose a method of based on PSO intelligent algorithm to optimize its parameters. While the design of maximum entropy and the improved OSTU between two fitness functions, as the basis of evaluation of the segmentation results. The results show that the PSO algorithm was used to optimize the PCNN model, not only to keep the high quality of the segmentation results, at the same time has a great improvement on the operation efficiency.