标题是学术论文非常重要的一个组成部分。本文对学术论文标题特征与其被引之间的关系进行探索性研究。首先提出4个假设,然后分析了1997年到2013年期间,在Journal of the Association for Information Science and Technology和Scientome...标题是学术论文非常重要的一个组成部分。本文对学术论文标题特征与其被引之间的关系进行探索性研究。首先提出4个假设,然后分析了1997年到2013年期间,在Journal of the Association for Information Science and Technology和Scientometrics两种期刊上发表的5375篇论文的相关数据。研究结果表明,对于发表时间较长的论文,论文标题长度越长,其被引用的次数越多;发表时间较短的论文,论文标题长度对其被引的影响不明显。从影响显著的角度看,标题中包括问号的论文,其被引用的次数越多。标题中的冒号对论文被引的影响不明显。高被引论文中,短标题的论文其被引次数较多,冒号和问号对高被引论文的被引没有明显影响。展开更多
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
文摘标题是学术论文非常重要的一个组成部分。本文对学术论文标题特征与其被引之间的关系进行探索性研究。首先提出4个假设,然后分析了1997年到2013年期间,在Journal of the Association for Information Science and Technology和Scientometrics两种期刊上发表的5375篇论文的相关数据。研究结果表明,对于发表时间较长的论文,论文标题长度越长,其被引用的次数越多;发表时间较短的论文,论文标题长度对其被引的影响不明显。从影响显著的角度看,标题中包括问号的论文,其被引用的次数越多。标题中的冒号对论文被引的影响不明显。高被引论文中,短标题的论文其被引次数较多,冒号和问号对高被引论文的被引没有明显影响。
基金Projects(2012AA010901,2012AA01A301)supported by National High Technology Research and Development Program of ChinaProjects(61272142,61103082,61003075,61170261,61103193)supported by the National Natural Science Foundation of ChinaProjects(B120601,CX2012A002)supported by Fund Sponsor Project of Excellent Postgraduate Student of NUDT,China
文摘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.
