用群体智能理论处理联合变换相关器输入面图像  

Preprocessing input plane image of joint transform correlator based on swarm intelligence method

作　　者：王勇[1,2] 朱明[1] 

机构地区：[1]中国科学院长春光学精密机械与物理研究所,吉林长春130033 [2]中国科学院研究生院,北京100039

出　　处：《光学精密工程》2010年第4期958-964,共7页Optics and Precision Engineering

基　　金：国家863高技术研究发展计划资助项目(No.2005AA778032)

摘　　要：为提高联合变换相关器的性能,将群体智能理论应用于联合变换相关器输入面图像的预处理。介绍了群体智能的概念,阐述了其潜在的分布式特征及符合自组织生物学过程的特点,在此基础上提出了一种基于群体智能的联合变换相关器输入面图像特征提取方法。介绍了经典联合变换相关器的工作原理并分析了其优缺点,对群体智能的预处理方法与几种常用的输入面图像预处理方法在联合变换相关器中的应用效果进行了比较研究。结果显示,输入面图像经灰度变换后对相关结果的改善并不明显,而sobel梯度处理、形态学方法和群体智能特征提取方法都有效改善了联合变换相关器的相关结果。针对所处理的联合图像,基于群体智能的联合变换相关器的输出面互相关峰值为4240,比sobel梯度处理联合变换相关器的互相关峰值高2906,比形态学联合变换相关器的输出面互相关峰值高1616,表明用群体智能理论处理联合变换相关器输入面图像,效果优于传统方法。In order to improve the performance of Joint Transform Correlators（JTCs）,the swarm intelligence method is used in a JTC to preprocess the joint plane image.Firstly,the concept of swarm intelligence method is introduced,and the feature of potential distribution and consistence with biological self-governing is illustrated.On the basis of above,an input plane image feature extraction method based on swarm intelligence is proposed,and then the basic theory of classicals JTC is introduced and its advantages and disadvantages are analyzed.Finally,the proposed image feature extraction method is used to preprocess the joint image in JTC.Processed results are compared with those of classical JTC and the JTC based on sobel operator in the extraction of joint image.Experimental results show that the crosscorrelation peak of swarm intelligence JTC is 4 240,which is larger 2 906 than that of the sobel JTC and larger 1 616 than that of the morphological JTC.These results show that preprocessing the input plane images by the proposed method is superior to those by traditional methods.

关 键 词：联合变换相关器 预处理 群体智能 灰度变换 微分算子 形态学 

分 类 号：O438.2[机械工程—光学工程] TP391.4[理学—光学]