检索结果-内蒙古大学图书馆

作者： Maio, D. Maltoni, D. Cappelli, R. Wayman, J.L. Jain, A.K. Biometric System Lab. DEIS University of Bologna via Sacchi 3 47023 Cessna Italy Biometric Test Center College of Engineering San Jose State University San Jose CA 95192 United States Pattern Recognition and Image Processing Laboratory Michigan State University East Lansing MI 48824 United States

Two years after the first edition, a new Fingerprint Verification Competition (FVC2002) was organized by the authors, with the aim of determining the state-of-the-art in this challenging pattern recognition application. The experience and the feedback received from FVC2000 allowed the authors to improve the organization of FVC2002 and to capture the attention of a significantly higher number of academic and commercial organizations (33 algorithms were submitted). This paper discusses the FVC2002 database, the test protocol and the main differences between FVC2000 and FVC2002. The algorithm performance evaluation will be presented at the 16th ICPR. © 2002 IEEE.

关键词： pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Robust recovery of eigenimages in the presence of outliers and occlusions

引用

Journal of Computing and Information Technology 1996年第1期4卷 25-38页

作者： Leonardis, Aleš Bischof, Horst Department for Pattern Recognition and Image Processing Technical University Vienna Treitlstraße 3/1832 ViennaA-1040 Austria Computer Vision Laboratory Faculty of Computer and Information Science University of Ljubljana Tržaška 25 Ljubljana1000 Slovenia

The basic limitations of the current appearance-based matching methods using eigenimages are non-robust estimation of coefficients and inability to cope with problems related to occlusions and segmentation. In this paper we present a new approach which successfully solves these problems. The major novelty of our approach lies in the way how the coefficients of the eigenimages are determined. Instead of computing the coefficients by a projection of the data onto the eigenimages. we extract them by a hvpothesize-and-test paradigm using subsets of image points. Competing hypotheses arc then subject to a selection procedure based on the Minimum Description Length principle. The approach enables us not only lo reject outliers and to deal with occlusions but also to simultaneously use multiple classes of eigenimages.

关键词： Principal component analysis

来源：评论

学校读者我要写书评

暂无评论

Self-adapting RF Stealth Signal Design Method in RATR

引用

Transactions of Nanjing University of Aeronautics and Astronautics 2021年第6期38卷 1020-1027页

作者： XIAO Yongsheng HUANG Lizhen HE Fengshou ZHOU Jianjiang Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Hangkong UniversityNanchang 330063P.R.China College of Electronic and Information Engineering Nanjing University of Aeronautics and AstronauticsNanjing 211106P.R.China Leihua Electronic Technology Research Institute Aviation Industry Corporation of ChinaLtd.Wuxi 214063P.R.China

Combining the mutual information theory and the sequential hypothesis testing(SHT)method,a selfadapting radio frequency(RF)stealth signal design method is proposed. The channel information is gained through the radar echo and feeds back to the radar system,and then the radar system adaptively designs the transmission waveform. So the close-loop system is formed. The correlations between these transmission waveforms are decreased because of the adaptive change of these transmission waveforms,and the number of illuminations is reduced for adopting the SHT,which lowers the transmission power of the radar system. The radar system using the new method possesses the RF stealth performance. Aiming at the application of radar automatic target recognition(RATR),experimental simulations show the effectiveness and feasibility of the proposed method.

关键词： radio frequency stealth sequential hypothesis testing mutual information close-loop system

来源：评论

学校读者我要写书评

暂无评论

Motion estimation and spatial-temporal filter-based infrared small target detection algorithm

引用

International Journal of Wireless and Mobile Computing 2015年第3期8卷 256-261页

作者： Wang, Zhonghua Liao, Yuan Liu, Qingping Li, Chunyong School of Information and Engineering Nanchang Hangkong University Nanchang Jiangxi Province330063 China Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Hangkong University Nanchang Jiangxi Province330063 China Department of Information Engineering Nanchang Hangkong University Nanchang330063 China

Under the effect of solar variation, atmospheric attenuation and thermal radiation distribution, the grey value of interference source is close to or equal to the target grey value. With the distance between the imaging system and the target being very long, the target is dotted or mottled in the image. In addition, the target motion is usually unknown and abrupt. All these factors cause the negative effect of small target detection. In this paper, we present a motion estimation and spatial-temporal filter-based algorithm to detect infrared small targets under complex background. The algorithm is divided into three steps: (1) researching the image transformation technology from greyscale pattern to entropy pattern;(2) studying the anisotropic smoothing and local constraint criterion of entropy flow;and (3) introducing spatial-temporal filter for detecting small targets through energy accumulation and trial search of targets. Compared to other methods, experimental results indicate that the proposed method can robustly detect small targets under complicated background. Copyright © 2015 Inderscience Enterprises Ltd.

关键词： Entropy

来源：评论

学校读者我要写书评

暂无评论

Towards multimodal response generation with exemplar augmentation and curriculum optimization

arXiv

引用

arXiv 2020年

作者： Lei, Zeyang Li, Zekang Zhang, Jinchao Meng, Fandong Feng, Yang Yang, Yujiu Niu, Cheng Zhou, Jie Tsinghua University China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Pattern Recognition Center WeChat AI Tencent Inc China

Recently, variational auto-encoder (VAE) based approaches have made impressive progress on improving the diversity of generated responses. However, these methods usually suffer the cost of decreased relevance accompanied by diversity improvements (Zhang et al., 2018b). In this paper, we propose a novel multimodal1 response generation framework with exemplar augmentation and curriculum optimization to enhance relevance and diversity of generated responses. First, unlike existing VAE-based models that usually approximate a simple Gaussian posterior distribution, we present a Gaussian mixture posterior distribution (i.e, multimodal) to further boost response diversity, which helps capture complex semantics of responses. Then, to ensure that relevance does not decrease while diversity increases, we fully exploit similar examples (exemplars) retrieved from the training data into posterior distribution modeling to augment response relevance. Furthermore, to facilitate the convergence of Gaussian mixture prior and posterior distributions, we devise a curriculum optimization strategy to progressively train the model under multiple training criteria from easy to hard. Experimental results on widely used SwitchBoard and DailyDialog datasets demonstrate that our model achieves significant improvements compared to strong baselines in terms of diversity and relevance. Copyright © 2020, The Authors. All rights reserved.

关键词： Curricula

来源：评论

学校读者我要写书评

暂无评论

Collaborative Weighting for Graph Convolutional Networks

Journal of Network Intelligence

引用

Journal of Network Intelligence 2023年第2期8卷 432-447页

作者： Chen, Yong Xie, Xiao-Zhu Weng, Wei Zhang, Shan-Dan Li, Tong College of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China College of Computer and Information Engineering Xiamen University of Technology Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen361024 China

Graph neural network (GNN), as a powerful method for graph representation, has attracted extensive research interest. Recently, Graph Convolutional Network (GCN) and Graph Attention Network (GAT) have shown superior performance on graph node classification and are considered as the most promising frameworks. Both GAT and GCN use a weighting mechanism in the information aggregation process. However, GCN is weighted by topological structure, while the node content is still unemployed. GAT is weighted by node content, while the topological structure remains being ignored. These weighting mechanism, which does not make full use of topology and node content, is not conducive to graph representation. In this paper, we propose a "Collaborative Weighting For Graph Convolutional Network" (CWGCN) that combines the advantages of GCN and GAT. Specifically, its weighting mechanism not only considers the topology of the graph, but also the node content. With this weighting method for representation learning, CWGCN achieves encouraging performance on a number of citation datasets in node classification. © 2023, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Classification (of information)

来源：评论

学校读者我要写书评

暂无评论

Detection Level of Apple Based on BP Neural Network

Detection Level of Apple Based on BP Neural Network

引用

International Conference on Information Sciences,Machinery,Materials and Energy（ICISMME 2015）

作者： Xiaoling LI Jimin YUAN School of Computer Science and Technology Chengdu University University Key Laboratory of Pattern Recognition and Intelligent information Processing The Department Computer Engineering Chengdu Aeronautic Vocational and Technical College

In view of the draw backs of apple grade identification in China,which still relies on photoelectric sorting and manual separation,this paper presents a processing method on the basis of the technology of computer vision and digital *** image processing technology,the researcher calculated the length of the long-short-axis,marked the location of it and calculated the 4 parameters,color,mean square,shape,size,as the key characteristics of the BP input of network to build a network and identify the level of apple through analysis of the external characteristics of *** optimum structure parameters of the BP neural network which had 9 hidden layer neurons were determined by RP training *** showed that average accuracy for fruit classification can reach 92.5% by using this model,and the executing time of microcomputer for grading of one apple is 9.3 *** method has the characteristics of high accuracy and good real-time performance.

关键词： characteristic parameter BP neural network apple detect level

来源：评论

学校读者我要写书评

暂无评论

Computation of the folkman number Fv(3,5;6)

引用

Journal of Combinatorial Mathematics and Combinatorial Computing 2012年 81卷 11-17页

作者： Shao, Zehui Liang, Meilian Pan, Linqiang Xu, Xiaodong School of Information Science and Technology Chengdu University Chengdu 610106 China Key Laboratory of Pattern Recognition and Intelligent Information Processing China School of Mathematics and Information Science Guangxi University Nanning 530004 China Key Laboratory of Image Processing and Intelligent Control Department of Control Science and Engineering Huazhong University of Science and Technology Wuhan 430074 China Guangxi Academy of Sciences Nanning Guangxi 530007 China

We prove that Fv(3,5;6) = 16, which solves the smallest open case of vertex Folkman numbers of the form Fv(3,k;k + 1). The proof uses computer algorithms.

关键词： Mathematical techniques

来源：评论

学校读者我要写书评

暂无评论

QT-based monitoring system of multi-functional laboratory

引用

Research Journal of Applied Sciences, Engineering and Technology 2012年第21期4卷 4480-4483页

作者： Xiaoling, Li Jimin, Yuan School of Computer Science and Technology Chengdu University Chengdu 610106 China Key University Key Laboratory of Pattern Recognition and Intelligent information Processing Sichuan Province China School of Computer Science and Technology Panzhihua University Panzhihua 617000 China

Regarding the embedded processor as the core, this study utilizes various cutting-edge technologies such as wireless LAN, USB interface, Bluetooth, multimedia, etc., to propose the design program of QT-based security monitoring system. Taking the lab environment in school as an example, this system has achieved the security monitoring, information transmission and control of certain equipment. Besides, it has cut the Linux kernel module reasonably and explored the touch screen, serial port, wireless LAN, Bluetooth, USB and other resources, thus realizing various functions, such as collection and processing of audio, video and security information and wireless communication. Thereby, users can carry out real-time monitoring for multiple locations through the wireless LAN. © Maxwell Scientific Organization, 2012.

关键词： Bluetooth

来源：评论

学校读者我要写书评

暂无评论

Epileptic EEG Signal Classification with ANFIS Based on Harmony Search Method

Epileptic EEG Signal Classification with ANFIS Based on Harm...

引用

International Conference on Computational Intelligence and Security

作者： Jing Wang X.Z. Gao Jarno M.A. Tanskanen Ping Guo Laboratory of Image Processing and Pattern Recognition Beijing Normal University Beijing China Department of Automation and Systems Technology Aalto University School of Electrical Engineering Finland Tampereen yliopisto - Hervannan kampus Tampere Pirkanmaa FI

In this paper, the Adaptive Neuro-Fuzzy Inference System (ANFIS) is used for the classification of the epileptic electroencephalogram (EEG) signals. The ANFIS combines the adaptation capability of the neural networks and the fuzzy logic-based qualitative approach together. A given input/output data set is deployed to construct a fuzzy inference system, whose membership function parameters are trained using a back propagation algorithm in combination with a least squares method. However, the training method sometimes may lead to local optima. We here propose a new strategy of hybrid training algorithm based on the fusion of the ANFIS and Harmony Search (HS), HS-ANFIS, which is adopted to tune all the parameters of the ANFIS. The validity of our method is verified by numerical experiments.

关键词： Electroencephalography Epilepsy Feature extraction Training pattern classification Adaptive systems Expert systems

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：