%0 Journal Article
%T Automatic Target Detection by Optimal Morphological Filters
%A Yu Nong
%A Wu Hao
%A Wu ChangYong
%A Li YuShu
%A <br>余农
%A 吴昊
%A 吴常泳
%A 李予蜀
%J 计算机科学技术学报
%D 2003
%I 
%X It is widely accepted that the design of morphological filters, which are optimal in some sense, is a difficult task. In this paper a novel method for optimal learning of morphological filtering parameters (Genetic training algorithm for morphological filters, GTAMF) is presented. GTAMF adopts new crossover and mutation operators called the curved cylinder crossover and master-slave mutation to achieve optimal filtering parameters in a global searching. Experimental results show that this method is practical, easy to extend, and markedly improves the performances of morphological filters. The operation of a morphological filter can be divided into two basic problems including morphological operation and structuring element (SE) selection. The rules for morphological operations are predefined so that the filter's properties depend merely on the selection of SE. By means of adaptive optimization training, structuring elements possess the shape and structural characteristics of image targets, and give specific information to SE. Morphological filters formed in this way become certainly intelligent and can provide good filtering results and robust adaptability to image targets with clutter background.
%K image analysis
%K morphological filter
%K genetic algorithm
%K optimizing calculation<br>图像合成
%K 形态滤波器
%K 自动目标探测
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=F57FEF5FAEE544283F43708D560ABF1B&aid=997999A7509C7F74B8D9EEC9AB959A16&yid=D43C4A19B2EE3C0A&vid=13553B2D12F347E8&iid=CA4FD0336C81A37A&sid=2B25C5E62F83A049&eid=2B25C5E62F83A049&journal_id=1000-9000&journal_name=计算机科学技术学报&referenced_num=0&reference_num=19