检索结果-内蒙古大学图书馆

2012 IEEE 11th International Conference on Signal processing(ICSP 2012)

作者： ZHANG Shaona HU Dong HU Yanting Education Ministry's Key Lab of Broadband Wireless Communication and Sensor Network Technology Nanjing University of Posts and Telecommunications The key lab of image processing and transmission of Jiangsu province Nanjing University of Posts and Telecommunications School of Telecommunication and Information Engineering Nanjing University of Posts and Telecommunications

ISBN: (纸本)9781467321969

A new scheme of object tracking with mean shift is put forward in this paper. At first, texture feature is fused in the processing by Local Ternary Pattern (LTP). Since LTP is sensitive to local noise, least median of squares (LMedS) algorithm is used to adaptively calculate the noise threshold for accurate estimation of the LTP texture information. Furthermore, target scale and orientation is estimated in case of partial occlusion or rotation, so as to realize robust object tracking. Experimental results show that the proposed algorithm can acquire robust tracking performance under complex background .

关键词： Object tracking, Mean Shift, LTP, LMedS

来源：评论

学校读者我要写书评

暂无评论

A Video Code Based on Distribution Compressive Sensing

引用

Procedia Engineering 2012年 29卷 3613-3618页

作者： Wu Ming-hu Zhu Xiu-chang Jiangsu Province Key Lab on Image Processing & Image Communication Nanjing University of Posts and Telecommunications Nanjing China School of Electrical and Electronic Engineering Hubei University of Technology Wuhan China

Compressed sensing (CS) is a new technique for simultaneous data sampling and compression. In this paper, we propose a new video coding algorithm based on Distributed Compressive Sampling(DCS) principles, where almost all computation burdens can be shifted to the decoder, resulting in a very lowcomplexity encoder. At the decoder, compressed video can be efficiently reconstructed. Our algorithm can be useful in those video applications that require very low complex encoders. Simulation results show that our scheme compares favorably with existing schemes at a much lower implementation cost.

关键词： Distributed Compressive Sampling sparsity distributed video coding

来源：评论

学校读者我要写书评

暂无评论

Adaptive dictionary learning for distributed compressive video sensing

International Journal of Digital Content Technology and its ...

引用

International Journal of Digital Content Technology and its Applications 2012年第4期6卷 141-149页

作者： Wu, Minghu Zhu, Xiuchang Gan, Zongliang Li, Xin Jiangsu Province Key Lab on Image Processing and Image Communication Nanjing University of Posts and Telecommunications Nanjing China School of Electrical and Electronic Enginesering Hubei University of Technology Wuhan China

Compressive sensing (CS) is a new technique for data sampling and compression simultaneously. In this paper, we propose a novel distributed video coding algorithm with dynamic measurement rate allocation based on compressive sensing principles, where almost all computation burdens can be shifted to the decoder, resulting in a very low-complexity encoder. So the proposed algorithm can be useful in those video applications that require very low complex encoders. At the decoder, the compressed video can be efficiently reconstructed with adaptive dictionary learning. The simulation results show that the proposed algorithm outperforms the distributed compressive video sensing with non-adaptive learning local dictionary and global dictionary.

关键词： Compressed sensing

来源：评论

学校读者我要写书评

暂无评论

Dynamic integral sliding mode for launch vehicle attitude control system

Dynamic integral sliding mode for launch vehicle attitude co...

引用

Chinese Control and Decision Conference, CCDC

作者： Zirui Wang Lei Liu Yongji Wang Zhishen Wang Key Lab for Image Processing & Intelligent Control Huazhong University of Science & Technology Wuhan China

As it is known that launch vehicle is facing a very harsh environment during the fight process. The challenge of various perturbations and uncertainties has lead to many traditional control methods' failure to meet the requirements of attitude control system. Due to the main advantage of sliding mode control's robustness to the system uncertainties and disturbances in the so-called sliding mode, it has been widely used in engineering. In this paper, regarding particularly on chattering problem, the authors developed a novel dynamic integral sliding mode control scheme and the comparative simulation results carried out with traditional dynamic integral sliding mode demonstrates the superiority of the newly designed control law.

关键词： Sliding mode control Rockets Attitude control Simulation Vehicle dynamics Vehicles

来源：评论

学校读者我要写书评

暂无评论

Prediction of tourists flow distribution based on transition probability matrix

Prediction of tourists flow distribution based on transition...

引用

International Conference on Information Science and Digital Content Technology

作者： Yuting Hu Rong Xie Wenjun Zhang Yi Xu Institute of Image Communication and Information Processing Shanghai Digital Media Processing and Transmission Key Lab Shanghai Jiaotong University China

ISBN: (纸本)9781467312882

A new prediction algorithm of tourists flow distribution based on transition probability matrix (TPM) is proposed in this paper. In order to analyze the visitor transition-behavior and the tourists distribution model, the tourists flow distribution of 5 zones at Shanghai Expo site is predicted based on the TPM, which is estimated by use of multivariate linear regression in optimization. The extensive experimental results verify the efficiency and the correctness of the proposed algorithm over the wavelet neural network prediction method.

关键词： TRANSITION PROBABILITIES tourists flow field third-party maintenance Prediction algorithms Linear Regression accuracy class

来源：评论

学校读者我要写书评

暂无评论

Robust integrated locally linear embedding

Robust integrated locally linear embedding

引用

7th Chinese Conference on Biometric Recognition, CCBR 2012

作者： Zhang, Li-Li Xie, Ying Luo, Bin Ding, Chris Tang, Jin Key Lab of Industrial Image Processing & Analysis of Anhui Province Hefei 230039 China School of Computer Science and Technology Anhui University Hefei 230601 China CSE Department University of Texas at Arlington Arlington TX 76019 United States of America

ISBN: (纸本)9783642355059

Many real life applications often bring much high-dimensional and noise-contaminated data from different sources. In this paper, we consider de-noising as well as dimensionality reduction by proposing a novel method named Robust Integrated Locally Linear Embedding. The method combines the two steps in LLE into a single framework and deals with de-noising by solving a l 2,1-l 2 mixed norm based optimization problem. We also derive an efficient algorithm to build the proposed model. Extensive experiments demonstrate that the proposed method is more suitable to exhibit relationship among data points, and has visible improvement in de-noising, embedding and clustering tasks. © 2012 Springer-Verlag.

关键词： Reduction

来源：评论

学校读者我要写书评

暂无评论

Denoising Diffusion Tensor images with Shearlet

Denoising Diffusion Tensor Images with Shearlet

引用

2012 IEEE 11th International Conference on Signal processing(ICSP 2012)

作者： Xiangfen Zhang Bao-Liang Lu Yan Ma Xiaozhong Xu Fangfang Wei Wenjie Xu Institute of Intelligent Computing & Image Processing College of Mechanical and Electronic EngineeringShanghai Normal University Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and EngineeringShanghai Jiaotong University MOE-Microsoft Key Lab for Intelligent Computing and Intelligent Systems Shanghai Jiao Tong University

ISBN: (纸本)9781467321969

Diffusion tensor imaging （DTI） is known to be the best non-invasive imaging modality in providing anatomical information as white-matter fiber bundles. However, the Gaussian noise introduced into the diffusion tensor images can bring serious impacts on tensor calculation and fiber tracking. To decrease the effects of the Gaussian noise, many denoising methods have been presented. In this paper, a shearlet based denosing strategy is introduced. To evaluate the efficiency of the proposed shearlet based denoising method in accounting for the Gaussian noise introduced into the images, the peak to peak signal-to-noise ratio （PSNR）, signal-to-mean squared error ratio （SMSE） and edge keeping index （Beta） metrics are adopted. The experiment results acquired from both the synthetic and real data indicate the good performance of our proposed filter.

关键词： diffusion tensor imaging shearlet transform wavelet transform PSNR SMSE denoising

来源：评论

学校读者我要写书评

暂无评论

Corrigendum to “Graph structure analysis based on complex network” [Digital Signal processing 22 (5) (2012) 713–725]

引用

Digital Signal processing 2013年第4期23卷 1332-1332页

作者： Jin Tang Bo Jiang Chin-Chen Chang Bin Luo School of Computer Science and Technology Anhui University Hefei 230039 Anhui China Key lab of Image Processing & Analysis of Anhui Province Hefei 230039 Anhui China Department of Information Engineering and Computer Science Feng Chia University Taiwan

来源：评论

学校读者我要写书评

暂无评论

Robust Mobile Spamming Detection via Graph Patterns

Robust Mobile Spamming Detection via Graph Patterns

引用

International Conference on Pattern Recognition

作者： Yuhang Zhao Zhaoxiang Zhang Yunhong Wang Jianyun Liu Intelligent Recognition and Image Processing Lab Beijing Key Laboratory of Digital Media School of Computer Science and Engineering Beihang University

ISBN: (纸本)9781467322164

Short message service (SMS) is now an indispensable way of social communication. However the mobile spam is getting increasingly serious, troubling users' daily life and ruining the service quality. We propose a novel approach for spam message detection based on mining the underlying social network of SMS activities. Comparing with strategies on keywords or flow detection, our network-based approach is more robust and difficult to defeat by human spammers. Various levels of features are employed to describe multiple aspects of the network, such as static structures, node activities and evolving situations. Experimental results on real dataset illustrate effectiveness of various features, showing our promising results.

关键词： data mining electronic messaging feature extraction mobile computing network theory (graphs) social networking (online) unsolicited e-mail

来源：评论

学校读者我要写书评

暂无评论

Pan-sharpening using weighted red-black wavelet

Pan-sharpening using weighted red-black wavelet

引用

International Conference on Pattern Recognition

作者： Qingjie Liu Yunhong Wang Zhaoxiang Zhang Lining Liu Intelligent Recognition and Image Processing Lab Beijing Key Laboratory of Digital Media School of Computer Science and Engineering Beihang University Beijing China

ISBN: (纸本)9781467322164

In this paper, we propose a new method for remote sensing image pan-sharpening which is based on weighted red-black (WRB) wavelet and adaptive principal component analysis (PCA), where the adaptive PCA is used to reduce spectral distortions and the utilization of WRB wavelet is used to extract the spatial details in PAN images. To reduce the artifacts and spectral distortions in the pan-sharpened images, which were caused by the local instabilities and dissimilarities in the PAN and MS images, a local process strategy incorporating detail enhancement is introduced. The proposed method is tested on two datasets both acquired by QuickBird and compared with the existing methods. Experimental results show that our method can provide promising fused MS images at a high spatial resolution.

关键词： Principal component analysis Correlation Remote sensing Multiresolution analysis Spatial resolution Discrete wavelet transforms

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：