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991.
一个有效的基于运动的视频检索系统 总被引:2,自引:0,他引:2
在视频摄取的过程中,摄象机的运动在某种程度上反映了当前视频的部分语义特征,所以通过对摄象机运动的提取能够更好地实现对视频的浏览以及基于内容的检索。针对以上应用,本文提出了一种用运动矢量的分布来提取摄相机运动的方法,测试后表明,该方法能有效地分析出多种相机运动并具有较好的鲁棒性。同时我们以此为基础实现了一个基于相机运动的视频检索系统。 相似文献
992.
基于XML的图形技术在WebGIS中的应用 总被引:7,自引:0,他引:7
空间信息技术和网络技术的融合,产生了一个基于Internet的WebGIS研究的热门课题。介绍了互联网信息时代的WebGIS的几种实现方案,认为传统的HTML在Web浏览器和服务器之间以图象格式传送JPG/GIF的地图数据,限制了WebGIS的性能,提出了一种基于可升级矢量图形SVG(Scalable Vector Graphics)和组件技术的WebGIS的解决方案。 相似文献
993.
图像集的离散度矩阵及其在人脸图像识别中的应用 总被引:2,自引:0,他引:2
本文提出了图像集离散度矩阵的基本概念及用于人脸图像识别的方法。该方法把同一类图像集中的各幅图像的信息矩阵的奇异值向量作为矩阵中的一列构成图像集特征矩阵。把这些矩阵组成一图像库,并找出图像集离散度矩阵和图像库离散度矩阵,从而计算出它们的最佳投影系数。然后,把测试样本的图像集特征矩阵与图像集库中的训练样本图像集的特征按投影系数相比较找出它的相似程度,从而进行人脸图像识别。 相似文献
994.
995.
Architecture transformations are frequently performed during software design and maintenance.However this activity is not well supported at a sufficiently abstract level.In this paper,the authors characterize architecture transformations using using graph rewriting rules,where architectures are represented in graph notations,Architectures are usually required to satisfy certain constraints during evolution.Therefore a way is presented to construct the sufficient and necessary condition for a transformatio to preserve a constraint.The condition can be verified before the application of the transformation.Validated transformations are guaranteed not to violate corresponding constraints whenever applied. 相似文献
996.
基于简化系统依赖图的静态粗粒度切片方法 总被引:8,自引:0,他引:8
基于系统依赖图是计算面向对象程序切片的一个有效方法.但是,系统依赖图的缺点是太复杂,而且在建立系统依赖图的过程中容易出错,一旦出现错误就可能导致切片结果的不准确.通过对系统依赖图进行简化,得到了简化的系统依赖图.它省略了那些表示输入参数和输出参数的结点和概括边.同时,还定义了一种面向对象程序的粗粒度切片概念,讨论了它的性质,分析了它与细粒度切片的关系,并基于简化的系统依赖图计算面向对象程序的粗粒度切片.最后还讨论了切片技术的简单实现. 相似文献
997.
998.
Shigeo Abe 《Pattern Analysis & Applications》2007,10(3):203-214
In this paper we discuss sparse least squares support vector machines (sparse LS SVMs) trained in the empirical feature space,
which is spanned by the mapped training data. First, we show that the kernel associated with the empirical feature space gives
the same value with that of the kernel associated with the feature space if one of the arguments of the kernels is mapped
into the empirical feature space by the mapping function associated with the feature space. Using this fact, we show that
training and testing of kernel-based methods can be done in the empirical feature space and that training of LS SVMs in the
empirical feature space results in solving a set of linear equations. We then derive the sparse LS SVMs restricting the linearly
independent training data in the empirical feature space by the Cholesky factorization. Support vectors correspond to the
selected training data and they do not change even if the value of the margin parameter is changed. Thus for linear kernels,
the number of support vectors is the number of input variables at most. By computer experiments we show that we can reduce
the number of support vectors without deteriorating the generalization ability.
Shigeo Abe received the B.S. degree in Electronics Engineering, the M.S. degree in Electrical Engineering, and the Dr. Eng. degree, all from Kyoto University, Kyoto, Japan in 1970, 1972, and 1984, respectively. After 25 years in the industry, he was appointed as full professor of Electrical Engineering, Kobe University in April 1997. He is now a professor of Graduate School of Science and Technology, Kobe University. His research interests include pattern classification and function approximation using neural networks, fuzzy systems, and support vector machines. He is the author of Neural Networks and Fuzzy Systems (Kluwer, 1996), Pattern Classification (Springer, 2001), and Support Vector Machines for Pattern Classification (Springer, 2005). Dr. Abe was awarded an outstanding paper prize from the Institute of Electrical Engineers of Japan in 1984 and 1995. He is a member of IEEE, INNS, and several Japanese Societies. 相似文献
Shigeo AbeEmail: |
Shigeo Abe received the B.S. degree in Electronics Engineering, the M.S. degree in Electrical Engineering, and the Dr. Eng. degree, all from Kyoto University, Kyoto, Japan in 1970, 1972, and 1984, respectively. After 25 years in the industry, he was appointed as full professor of Electrical Engineering, Kobe University in April 1997. He is now a professor of Graduate School of Science and Technology, Kobe University. His research interests include pattern classification and function approximation using neural networks, fuzzy systems, and support vector machines. He is the author of Neural Networks and Fuzzy Systems (Kluwer, 1996), Pattern Classification (Springer, 2001), and Support Vector Machines for Pattern Classification (Springer, 2005). Dr. Abe was awarded an outstanding paper prize from the Institute of Electrical Engineers of Japan in 1984 and 1995. He is a member of IEEE, INNS, and several Japanese Societies. 相似文献
999.
Paolo Baldan Fabio Gadducci Ugo Montanari 《Electronic Notes in Theoretical Computer Science》2007,176(1):85
In the theory of graph rewriting, the use of coalescing rules, i.e., of rules which besides deleting and generating graph items, can coalesce some parts of the graph, turns out to be quite useful for modelling purposes, but, at the same time, problematic for the development of a satisfactory partial order concurrent semantics for rewrites. Rewriting over graphs with equivalences, i.e., (typed hyper)-graphs equipped with an equivalence over nodes provides a technically convenient replacement of graph rewriting with coalescing rules, for which a truly concurrent semantics can be easily defined. The expressivity of such a formalism is tested in a setting where coalescing rules typically play a basic role: the encoding of calculi with name passing as graph rewriting systems. Specifically, we show how the (monadic fragment) of the solo calculus, one of the dialect of those calculi whose distinctive feature is name fusion, can be encoded as a rewriting system over graph with equivalences. 相似文献
1000.
Aliaksei Kerhet Michele Magno Francesco Leonardi Andrea Boni Luca Benini 《Journal of Real-Time Image Processing》2007,2(4):331-342
In this paper we propose MicrelEye, a wireless video node for cooperative distributed video processing applications that involve
image classification. The node is equipped with a low-cost VGA CMOS image sensor, a reconfigurable processing engine (FPGA,
Microcontroller, SRAM) and a Bluetooth 100-m transceiver. It has a size of few cubic centimeters and its typical power consumption
is approximately ten times less than that of typical commercial DSP-based solutions. As regards classification, a highly optimized
hardware-oriented support vector machine-like (SVM-like) algorithm called ERSVM is proposed and implemented. We describe our
hardware and software architecture, its performance and power characteristics. The case study considered in this paper is
people detection. The obtained results suggest that the present technology allows for the design of simple intelligent video
nodes capable of performing classification tasks locally.
相似文献
Luca BeniniEmail: |