摘要
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。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。该方法在大数据背景下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。
The paper gave a new attempts to solve the problem of image matching, namely color image matching based on im- proved parallel particle swarm optimization. It established the matching strategy and the mathematical model of color image matching problem. The result of the simulation experiments applying color image matching based on improved parallel particle swarm optimization algorithm which realized by . NET TPL/PLINQ compared the result that based on the standard particle swarm optimization algorithm. It verified 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.
出处
《计算机应用研究》
CSCD
北大核心
2016年第8期2543-2546,共4页
Application Research of Computers
基金
国家自然科学基金资助项目(71401106)
上海市一流学科建设资助项目(S1201YLXK)
沪江基金资助项目(B14006)
