Image Segmentation Based On Particle Swarm Optimization And Ant Colony Algorism Based On GPU-Accelerated

From the middle of 1950s when people set up the bionics, People always got the enlightenment from the mechanism of biological development, created many new method to solve combination optimization problem, such as Ant Colony Algorism and Particle Swarm Optimization and so on, both of them are intelligent and general -purpose global optimization method for large-scale parallelization, and they have good result to solve the problem which are complication, large-scale, non-linear and difficulties of modeling in engineering optimization.Image segmentation is the first and foremost problem in image analyzing and mode recognition, and is also a typical stumbling block in image processing, the traditional OTSU confined the development of this algorism to an extent. In order to raise its speed, we combined the method of PSO and OTSU based on valves and therefore proposed a OTSU based on PSO. As we achieve an effective segmentation, we also raised the speed of the parallel searching system.Graphics processing unit (GPU) has been developing rapidly in recent years at a speed over Moor's law, and as a result, various applications associated with computer graphics advance greatly. At the same time, the highly processing power, parallelism and programmability available nowadays on the contemporary GPU provide an ideal platform on which the general-purpose such as digital image processing computation could be made.The thesis inspire from it which give a parallel ACO based on GPU-Accelerated for that the slow speed of the traditional ACO, it converts the progress of working-out into the progress of texture-rendering based on GPU, making ACO greatly accelerated in it and apply it to actual problem, As achieving a good optimization effect, it also increases the population of ACO solve problems, speeds up its running and provides ordinary user with a feasible ACO solution.