基于改进并行粒子群算法的彩色图像匹配

本文给出了对解决图像匹配问题的一种新尝试，即基于改进并行粒子群算法的彩色图像匹配。本文提出和建立对彩色图像匹配问题的匹配策略和数学模型，应用改进并行粒子群算法(基于 .Net任务并行库(TPL)/PLINQ实现并行化)进行仿真实验并将实验结果与标准粒子群算法下的彩色图像匹配问题的实验结果进行比较，验证了算法的实用性和有效性。在大数据背景下的智能算法的应用方面迈进了一小步，同时也给本身研究不多的彩色图像匹配问题提供了一种新的且可行的解决方法。

Color image matching technology based on improved parallel particle swarm optimization

The paper gives a new attempts to solve the problem of image matching, namely color image matching based on improved parallel particle swarm optimization. The matching strategy and the mathematical model of color image matching problem is established in this paper. The result of the simulation experiments applying color image matching based on improved parallel particle swarm optimization algorithm which realized by .Net TPL/PLINQ compares the result that based on the standard particle swarm optimization algorithm. It verifies the validity of the algorithm. Therefore this paper not only generalizes application of the particle swarm optimization, but also provides a new solution to the problem of color image matching to which there are not many solutions feasible.