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一种生成具有变量标识的高级Petri网可达树的算法 被引量:2
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作者 林闯 张彤 《计算机学报》 EI CSCD 北大核心 1991年第8期596-604,共9页
Petri网动态性质的考察一般基于网不变量(Net Invariants)和可达树(Reachability Tree).这两个概念已被扩展到高级Petri网中.高级Petri网可达集空间随着网的复杂性而指数性增长是计算可达树问题中的一个主要难 点.本文定义了具有变量标... Petri网动态性质的考察一般基于网不变量(Net Invariants)和可达树(Reachability Tree).这两个概念已被扩展到高级Petri网中.高级Petri网可达集空间随着网的复杂性而指数性增长是计算可达树问题中的一个主要难 点.本文定义了具有变量标识的高级Petri网并给出了构造该类网的可达树的算法.本文的算法以变量标识的等价关系(equivalent relation)和覆盖关系(covering relation)为基础,明显地简化了可达集空间.个体标识的信息可从变量标识的定义域中获得. 展开更多
关键词 PETRI网 可达树 算法 变量标识
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Fast image matching algorithm based on affine invariants
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作者 张毅 卢凯 高颖慧 《Journal of Central South University》 SCIE EI CAS 2014年第5期1907-1918,共12页
Feature-based image matching algorithms play an indispensable role in automatic target recognition (ATR). In this work, a fast image matching algorithm (FIMA) is proposed which utilizes the geometry feature of ext... Feature-based image matching algorithms play an indispensable role in automatic target recognition (ATR). In this work, a fast image matching algorithm (FIMA) is proposed which utilizes the geometry feature of extended centroid (EC) to build affine invariants. Based on at-fine invariants of the length ratio of two parallel line segments, FIMA overcomes the invalidation problem of the state-of-the-art algorithms based on affine geometry features, and increases the feature diversity of different targets, thus reducing misjudgment rate during recognizing targets. However, it is found that FIMA suffers from the parallelogram contour problem and the coincidence invalidation. An advanced FIMA is designed to cope with these problems. Experiments prove that the proposed algorithms have better robustness for Gaussian noise, gray-scale change, contrast change, illumination and small three-dimensional rotation. Compared with the latest fast image matching algorithms based on geometry features, FIMA reaches the speedup of approximate 1.75 times. Thus, FIMA would be more suitable for actual ATR applications. 展开更多
关键词 affine invariants image matching extended centroid ROBUSTNESS PERFORMANCE
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