摘要
为提高图像匹配速度和精度,利用灰色关联分析理论和人工蜂群算法,提出一种抗噪性较好的快速图像匹配方法,简称GABC法。该方法将模板图像和当前搜索位置子图的直方图信息作为参考序列和比较序列,设计基于灰色关联度的适应度函数;然后对人工蜂群算法中的初始种群个体的分布进行优化,以提高收敛速度;接着,人工蜂群通过个体分工与信息共享,实现群体智能的高效并行寻优能力,快速逼近最佳匹配位置。实验显示,该方法在保证了一定匹配精度的情况下,明显提高了匹配速度和抗噪性。
To increase the speed and accuracy of image matching,suggest a new method,which is based on grey relational theory and artificial bee colony algorithm（GABC）.In the method,a referential sequence and a comparative sequence are respectively constructed by the histogram information of the template image and the current searching subimage.Then,based on the grey relational degree between the two sequences,a fitness function of artificial bee colony algorithm is designed.Secondly,optimize individuals＇ distribution of the initial bee swarm to improve the convergence speed.The bees currently approach to the best matching position through labor division and information sharing of swarm intelligence.The experimental results indicate that the proposed method not only provides with precise positions,but also obviously increases the matching speed and noise immunity.
出处
《计算机技术与发展》
2010年第10期78-81,共4页
Computer Technology and Development
基金
国家自然科学基金(60803088)
中央高校基本科研业务费专项资金重点项目(GK200901006)
陕西省自然科学基金(2007D07)
关键词
图像匹配
人工蜂群算法
灰色关联分析
适应度函数
image matching
artificial bee colony algorithm
grey relational analysis
fitness function
