检索结果-内蒙古大学图书馆

International Journal of Multimedia and Ubiquitous Engineering 2014年第3期9卷 257-268页

作者： Li, Xiaoling Yuan, Jimin School of Computer Science and Technology Chengdu University Chengdu 610106 China University Key Laboratory of Pattern Recognition and Intelligent information Processing Chengdu 610106 China The Department Computer Engineering Chengdu Aeronautic Vocational and Technical College Chengdu 610000 China

The existing safety and health monitoring methods for bridge construction are mainly manual monitoring and wired monitoring with many disadvantages, such as low efficiency, poor accuracy, great implementation difficulty, short bridge age, etc. To solve such problems, the paper provides a design scheme for the bridge construction safety and health monitoring System based on wireless sensor networks. In the system, various sensors are mainly used to collect data about the wind, temperature, traffic load, humidity, static position, static displacement and external load response, and then send to the host by route and gateway nodes. After data analysis and processing, the safety of the bridge is evaluated to help the personnel make correct bridge safety precautions. With such advantages as low power demand, wireless transmission of monitoring data, etc., the system is suitable for large range of promotion. © 2014 SERSC.

关键词： Data handling

来源：评论

学校读者我要写书评

暂无评论

image quality assessment with mean squared error in a log based perceptual response domain

Image quality assessment with mean squared error in a log ba...

引用

IEEE China Summit & International Conference on Signal and Information processing (ChinaSIP)

作者： Wufeng Xue Xuanqin Mou Institute of Image Processing and Pattern Recognition Xi'an Jiaotong University Xi'an China

Up to now, there existing a lot of models that predict subjective quality of the contents of natural images which have undergone some unknown distortion procedures. These models, no matter fall in to the bottom-up mechanism or belong to the top-down functional modelling, fail to provide an easy-applied and reliable solution. The complex computation procedure prevents them from being widely used in related image processing areas such as image enhancement, image reconstruction and video coding. In the present work, we start from a two stage nonlinear perception model, which transforms the input image into a decorrelated one and then further reduces the redundancy between neighboring pixels by another nonlinear normalization procedure which transforms the previous output into a perceptual response domain. The final quality prediction is computed as the Euclid distance of the reference image and the distorted one in this response domain, this will make the new model be readily applied in other areas.

关键词： Abstracts Indexes PSNR Visualization image processing

来源：评论

学校读者我要写书评

暂无评论

Semi-supervised Fisher-Markov Selector via Manifold Regularization

Semi-supervised Fisher-Markov Selector via Manifold Regulari...

引用

2012 International Conference on Applied Informatics and Communication(ICAIC 2012)

作者： Dong Chen Lin Zhu JieYang Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Ministry of Education of China

Feature subset selection is an important approach to deal with high-dimensional data. But selecting the best subset of data is NP hard. So most of feature selection methods cannot handle high-dimensional data efficiently, or they can only obtain local optimum instead of global optimum. In these cases, when the data consist of both labeled and unlabeled data, semi-supervised feature selection can make full use of data information. In this paper, we introduce a novel semi-supervised feature selection algorithm, which is a filter method based on Fisher-Markov selector, thus ours can achieve global optimum and computational efficiency under certain kernels.

关键词： Feature selection, Dimensionality reduction, Manifold regularization, Semi-supervised learning

来源：评论

学校读者我要写书评

暂无评论

Optimal classification of epileptic EEG signals using neural networks and harmony search methods

Journal of Software

引用

Journal of Software 2014年第1期9卷 230-239页

作者： Gao, Xiao-Zhi Wang, Jing Tanskanen, Jarno M. A. Bie, Rongfang Wang, Xiaolei Guo, Ping Zenger, Kai College of Information Engineering Shanghai Maritime University China Department of Automation and Systems Technology Aalto University School of Electrical Engineering Finland Laboratory of Image Processing and Pattern Recognition Beijing Normal University China Department of Biomedical Engineering Tampere University of Technology Finland School of Foundational Education Peking University Health Science Center China

In this paper, the Harmony Search (HS)-aided BP neural networks are used for the classification of the epileptic electroencephalogram (EEG) signals. It is well known that the gradient descent-based learning method can result in local optima in the training of BP neural networks, which may significantly affect their approximation performances. Three HS methods, the original version and two new variations recently proposed by the authors of the present paper, are applied here to optimize the weights in the BP neural networks for the classification of the epileptic EEG signals. Simulations have demonstrated that the classification accuracy of the BP neural networks can be remarkably improved by the HS method-based training. © 2014 ACADEMY PUBLISHER.

关键词： Optimization

来源：评论

学校读者我要写书评

暂无评论

Visual object tracking with online learning on Riemannian manifolds by one-class support vector machines

Visual object tracking with online learning on Riemannian ma...

引用

作者： Yun, Yixiao Fu, Keren Gu, Irene Yu-Hua Yang, Jie Dept. of Signals and Systems Chalmers Univ. of Technology Gothenburg41296 Sweden Inst. of Image Processing and Pattern Recognition Shanghai Jiao Tong Univ. Shanghai200240 China

ISBN: (纸本)9781479957514

This paper addresses issues in video object tracking. We propose a novel method where tracking is regarded as a one-class classification problem of domain-shift objects. The proposed tracker is inspired by the fact that the positive samples can be bounded by a closed hypersphere generated by one-class support vector machines (SVM), leading to a solution for robust learning of target model online. The main novelties of the paper include: (a) represent the target model by a set of positive samples as a cluster of points on Riemannian manifolds;(b) perform online learning of target model as a dynamic cluster of points flowing on the manifold, in an alternate manner with tracking;(c) formulate geodesic-based kernel function for one-class SVM on Riemannian manifolds under the log-Euclidean metric. Experiments are conducted on several videos, results have provided support to the proposed method. © 2014 IEEE.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

When Face recognition Meets with Deep Learning: An Evaluation of Convolutional Neural Networks for Face recognition

When Face Recognition Meets with Deep Learning: An Evaluatio...

引用

International Conference on Computer Vision Workshops (ICCV Workshops)

作者： Guosheng Hu Yongxin Yang Dong Yi Josef Kittler William Christmas Stan Z. Li Timothy Hospedales Centre for Vision Speech and Signal Processing University of Surrey UK Indicates equal contribution LEAR team Inria Grenoble Rhone-Alpes Montbonnot France Electronic Engineering and Computer Science Queen Mary University of London UK Chinese Academy of Sciences Center for Biometrics and Security Research & National Laboratory of Pattern Recognition China

Deep learning, in particular Convolutional Neural Network (CNN), has achieved promising results in face recognition recently. However, it remains an open question: why CNNs work well and how to design a 'good' architecture. The existing works tend to focus on reporting CNN architectures that work well for face recognition rather than investigate the reason. In this work, we conduct an extensive evaluation of CNN-based face recognition systems (CNN-FRS) on a common ground to make our work easily reproducible. Specifically, we use public database LFW (Labeled Faces in the Wild) to train CNNs, unlike most existing CNNs trained on private databases. We propose three CNN architectures which are the first reported architectures trained using LFW data. This paper quantitatively compares the architectures of CNNs and evaluates the effect of different implementation choices. We identify several useful properties of CNN-FRS. For instance, the dimensionality of the learned features can be significantly reduced without adverse effect on face recognition accuracy. In addition, a traditional metric learning method exploiting CNN-learned features is evaluated. Experiments show two crucial factors to good CNN-FRS performance are the fusion of multiple CNNs and metric learning. To make our work reproducible, source code and models will be made publicly available.

关键词： Face recognition Face Databases Measurement Training Object recognition Convolutional codes

来源：评论

学校读者我要写书评

暂无评论

Multi-view face detection based on multi-features adaboost collaborative learning algorithm

Multi-view face detection based on multi-features adaboost c...

引用

2014 International Conference on Applied Sciences, Engineering and Technology, ICASET 2014

作者： Huang, Rong Bing Zhang, Hong Shu, Chang Ming Key Laboratory of Pattern Recognition and Intelligent Information Processing of Sichuan Chengdu University Sichuan 610106 China School of Information Science and Technology Chengdu University Sichuan 610106 China

ISBN: (纸本)9783038351849

In view of the multi-view face detection problem under complex background, an improved face detection method based on multi-features boosting collaborative learning algorithm integrating local binary pattern (LBP) is presented. Firstly, facial skin color information is used to exclude most of the background regions. Then, Haar-like feature and LBP feature are extracted from facial candidate regions and inputted into a modified AdaBoost algorithm to obtain a strong classifier. Lastly, in order to improve the detection speed, pyramid classifier system structure is adopted to determine the face. The experimental results on CMU standard test set and life photos, the proposed method has achieved the rapid detection of multi-view face image. © (2014) Trans Tech Publications, Switzerland.

关键词： Face recognition

来源：评论

学校读者我要写书评

暂无评论

A robust method for high-accuracy microbe identification based on image processing

A robust method for high-accuracy microbe identification bas...

引用

IEEE International Conference on Progress in Informatics and Computing (PIC)

作者： Mingqi Cao Yuncai Liu Shanghai Jiao Tong University Institute of Image Processing and Pattern Recognition Shanghai China

ISBN: (纸本)9781479920327

We present a novel fast method based on computer vision to identify microbe The proposed method is simple but absolutely effective It combines approximate parallel light source and industrial camera, to automatically accomplish the bacteria identification and monitor the growing states of bacteria during the progress of a drug sensitive test. Based on this method, the color information and turbidity information, which reflect the primary information of drug sensitive tests, can be obtained fast, while processing efficiency can be as high as hundreds of milliseconds per frame. The performance of our method is significantly accurate and robust.

关键词： Feature extraction image color analysis Drugs Monitoring image edge detection Noise Media

来源：评论

学校读者我要写书评

暂无评论

Unsupervised detection of different SAR images based on improved NSCT domain image fusion algorithm

引用

Guangdianzi Jiguang/Journal of Optoelectronics Laser 2015年第10期26卷 2023-2030页

作者： Zhang, Yi-Chen Jia, Zhen-Hong Qin, Xi-Zhong Yang, Jie Kasabov, Nikola College of Information Science and Engineering Xinjiang University Urumqi China Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Shanghai China Knowledge Engineering and Discovery Research Institute Auckland University of Technology Aukland New Zealand

In order to reduce the total errors and obtain a better result in change detection with different SAR images, an unsupervised change detection algorithm is proposed in this paper. The technique is based on nonsubsampled contourlet transform (NSCT) domain with an improved fusion algorithm and fuzzy local information C-means clustering (FLICMC). First of all, mean-ratio operation and log-ratio operation are adopted to obtain the difference image, by using two images acquired in the same geographical area at different moments. Secondly, the two difference images are transformed into NSCT domain. The low frequency coefficients of two images of difference images respectively are fused by the rule of the maximum gradient quadratic sum;the high frequency coefficients of two images of difference images respectively are fused by the rule of the weighted fusion. And the final difference image can be got by using NSCT inverse transform. Finally, on the basis of FLICMC algorithm, the final difference image is divided into change area and unchanged area. According to the research between the real remote sensing data set and the simulated remote sensing data set, the experimental results show that the proposed algorithm can effectively suppress speckle noise impact on the detection results, and is not limited by the statistical distribution of change class and unchanged class. It has great applicability and doesn't require prior knowledge. So the proposed algorithm can get better detection effect. Compared with DWT2-FLICMC and MRF-FCMC, the proposed algorithm can obtain higher detection accuracy and shorter running time. ©, 2015, Board of Optronics Lasers. All right reserved.

关键词： Synthetic aperture radar

来源：评论

学校读者我要写书评

暂无评论

A new network traffic prediction algorithm based on discrete queue

引用

Journal of Computational Information Systems 2015年第12期11卷 4255-4261页

作者： Zhang, Hong Hu, Jun Shen, Yuncheng Key Laboratory of Pattern Recognition and Intelligent Information Processing Chengdu China College of Information Science and Technology Chengdu University Chengdu China College of Computer Science Sichuan University Chengdu China School of Traffic and Transportation Beijing Jiaotong University Beijing China Faculty of Computer Science Chengdu Normal University Chengdu China College of Information Science and Technology Zhaotong University Zhaotong China

In order to verify the network traffic decline because by node breakdown, this paper proposes a new type of prediction algorithm (Prediction algorithm based on Discrete-Queue for FARIMA model, PDF). At first, the mathematic formula for queuing situation and average queue length in steady state is derived with queuing theory in this algorithm based on discrete time, and the prediction method is established by FARIMA Model. Then, a simulation was conducted to research on the relationships between average queue length and service rate. The result shows that it has good adaptability, and the standard deviation between prediction traffic and original traffic is 10.18. ©, 2015, Binary Information Press. All right reserved.

关键词： Forecasting

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：