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检索条件"机构=Image Processing and Image Communications Key Lab"

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Analysis of communication systems using iterative methods based on Banach's contraction principle

Analysis of communication systems using iterative methods ba...

引用

2007 6th International Conference on Information, communications and Signal processing, ICICS

作者： Azari Soufiani, H. Saberian, M.J. Akhaee, M.A. Nasiri Mahallati, R. Marvasti, F. Multimedia Signal Sound and Image Processing Lab. Advanced Communications Research Institute of Sharif University of Technology Tehran Iran

ISBN: (纸本)1424409837

In this paper, the application of a well known mathematical theorem, Banach's fixed point theorem [1], is investigated in iterative signal processing in communications. In most practical communication systems some sort of a contraction mapping is used to enhance the operation of the system. Thus, using a suitable iterative approach, one can set the system in its fixed point and hence, the distortion produced in the transmitter, channel and the receiver can be compensated. In other words, a loosely designed transceiver can be enhanced by an iterative method. In order to verify the truth of the proposed iterative method, the distortion of A/D and D/A converters is compensated at the receiver. Although analyses are performed on a linear system, they could be generalized to non linear ones. ©2007 IEEE.

关键词： Iterative methods

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Alpha wave attention EEG analysis based on the multiscale Jensen-Shannon divergence

引用

2nd International Conference on Materials Science and Mechanical Engineering, ICMSME 2014

作者： Zhang, Zheng Xia Xu, Si Qiu Zhou, Er Ning Huang, Xiao Lin Wang, Jun Image Processing and Image Communications Key Lab College of Geo and Bio Information Nanjing Univ of Posts and Telecomm Nanjing 210003 China Institute of Biomedical Electronic Engineering School of Electronic Science and Engineering Nanjing University Nanjing 210093 China

ISBN: (纸本)9783038351689

The article adopted the multiscale Jensen - Shannon Divergence method for EEG complexity analysis. Then the study found that the method can distinguish between three different status (eyes closed, count, in a daze) acquisition of Alpha EEG time series, which shows three different states of Alpha EEG time series having significant differences. In each state of the three different states (eyes closed, count, in a daze), we aimed at comparing and analyzing the statistical complexity of EEG time series itself and the statistical complexity of EEG time series proxy data, finding that there are large amounts of nonlinear time series in the Alpha EEG signals. This method is also fully proved that the multiscale JSD algorithm can be used to analyze EEG signals. Attention statistical complexity can be used as a measure of brain function parameter, which can be applied to the auxiliary clinical brain function evaluation in the future. © (2014) Trans Tech Publications, Switzerland.

关键词： Time series

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Mixed image de-noising method of Bayesian estimation and wavelet based on alpha-stable distribution

Mixed image de-noising method of Bayesian estimation and wav...

引用

2008 IEEE International Conference Neural Networks and Signal processing, ICNNSP

作者： Zhijian, Wang Xuelong, Hu Cheng, Li Liping, Wang School of Information Engineering Yangzhou University Yangzhou Jiangsu 225009 China Jiangsu Province Key Lab. for Image Processing and Image Communication Nanjing University of Posts and Communications Nanjing Jiangsu 210003 China Yangzhou Jiangsu 225001 China

ISBN: (纸本)9781424423118

This paper introduces a new kind of recovery method which is the combination of Bayesian estimation and wavelet threshold. Wavelet coefficients of signals show strong characteristics of the non-Gauss statistics, its probability density function can be modeled by alpha-stable priori distribution. This paper uses Bayesian estimation to obtain the low frequency coefficients, and deals with high frequency coefficients with the wavelet soft threshold, then restore signal by wavelet inverse transform. Bayesian estimation well retains the marginal information of signals, but the signal is still not very smooth recovery and wavelet soft threshold can maintain smooth characteristic. The mixed noise elimination methods of Bayesian estimation and wavelet enhance the peak SNR much greater than wavelet threshold, which has better recovery effect. ©2008 IEEE.

关键词： Probability density function

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Attention electroencephalogram analysis based on Jensen-Shannon Divergence analysis

Attention electroencephalogram analysis based on Jensen-Shan...

引用

2013 International Conference on Bio-Medical Materials and Engineering, ICBME 2013

作者： Zheng, Zheng Zhong Zhao, Jun Chang Huang, Xiao Lin Wang, Jun Image Processing and Image Communications Key Lab College of Geo and Bio Information Nanjing Univ of Posts and Telecomm Nanjing 210003 China Institute of Biomedical Electronic Engineering School of Electronic Science and Engineering Nanjing University Nanjing 210093 China

ISBN: (纸本)9783037857557

In this paper, the complexity analysis method based on Jensen-Shannon Divergence was used to calculate the complexity of the Idle states, the close eyes states and the count numbers states electroencephalogram. The study found that the JSD value of close eyes states EEG was highest, followed by that of the Idle states EEG, and that of the count numbers states EEG was minimum. The result can be used to assisted clinical diagnosis. © (2013) Trans Tech Publications, Switzerland.

关键词： Electroencephalography

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A Combined Texture-Shape Global 3D Feature Descriptor for Object Recognition and Grasping 3

A Combined Texture-Shape Global 3D Feature Descriptor for Ob...

引用

3rd International Conference on Industrial Informatics - Computing Technology, Intelligent Technology, Industrial Information Integration, ICIICII 2017

作者： Fan, Zhun Li, Zhongxing Li, Wenji You, Yugen Chen, Wenzhao Li, Chong Key Lab of Digital Signal and Image Processing of Guangdong Province Shantou University Guangdong Shantou515063 China

ISBN: (纸本)9781538624340

This paper presents a global 3D feature descriptor for object recognition and grasping. The proposed descriptor stems from the clustered viewpoint feature histogram (CVFH) feature descriptor. Since the CVFH feature descriptor relies on shape information only, it obtains a poor performance when recognizing the objects with similar shapes. In order to improve the robustness and accuracy of object recognition, we extend CVFH feature descriptor with color information. Then this new global 3D feature descriptor is tested for multiple classes of 3D object classification by using support vector machines (SVM), and it is evaluated with a public dataset and real scenes respectively. The experimental results show that the proposed descriptor outperforms the CVFH feature descriptor in terms of recognition rate. Finally we utilize the proposed descriptor on our grasping system to recognize and grasp the objects, showing that the grasping system can accomplish the tasks well. © 2017 IEEE.

关键词： Support vector machines

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Research on attention eeg based on the multiscale jensen-shannon divergence

Research on attention eeg based on the multiscale jensen-sha...

引用

3rd International Conference on Mechanical Automation and Materials Engineering, ICMAME 2014

ISBN: (纸本)9783038350941

The article adopted the multiscale Jensen-Shannon Divergence analysis method for EEG complexity analysis. Then the study found that this method can distinguish between three different status (Eyes closed, count, in a daze) acquisition of EEG time series. It showed that three different states of EEG time series have significant differences. In each state of the three different states (Eyes closed, count, in a daze), we aimed at comparing and analyzing the statistical complexity of EEG time series itself and the statistical complexity of EEG time series shuffled data. It was found that there are large amounts of nonlinear time series in the EEG signals. This method is also fully proved that the multiscale JSD algorithm can be used to analyze attention EEG signals. The multiscale Jensen-Shannon Divergence statistical complexity can be used as a measure of brain function parameter, which can be applied to the auxiliary clinical brain function evaluation in the future. © (2014) Trans Tech Publications, Switzerland.

关键词： Time series

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Multiscale jensen-shannon divergence based analysis of beta wave attention EEG

Multiscale jensen-shannon divergence based analysis of beta ...

引用

2014 International Conference on Mechanics and Mechatronics, ICMM 2014

ISBN: (纸本)9783038351627

The article adopted the multiscale Jensen-Shannon Divergence analysis method for EEG complexity analysis, then the study found that this method can distinguish between three different status (Eyes closed, count, in a daze) acquisition of Beta EEG time series, shows three different states of Beta EEG time series have significant differences. In each state of the three different states (Eyes closed, count, in a daze), we are aimed at comparing and analyzing the statistical complexity of EEG time series itself and the statistical complexity of EEG time series shuffled data, finding that there are large amounts of nonlinear time series in the Beta EEG signals. This method is also fully proved that the multi-scale JSD algorithm can be used to analyze EEG signals, attention statistical complexity can be used as a measure of brain function parameter, which can be applied to the auxiliary clinical brain function evaluation in the future. © (2014) Trans Tech Publications, Switzerland.

关键词： Time series

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A simplified 3D gaze tracking technology with stereo vision

A simplified 3D gaze tracking technology with stereo vision

引用

2010 International Conference on Optoelectronics and image processing, ICOIP 2010

作者： Zhang, Ke Zhao, Xinbo Ma, Zhong Man, Yi Shaanxi Provincial Key Lab. of Speech and Image Information Processing Northwestern Polytechnical University Xi'an China

ISBN: (纸本)9780769542522

A simplified 3D gaze tracking technology with stereo vision has been developed in this paper. A pair of stereo cameras and two point light sources are used to estimate 3D gaze of user's eye. Compared with other 3D systems, there are two improvements to simplify the whole system in this paper: First, 3D gaze is estimated as the line linking 3D cornea center and the virtual image of pupil center using the first Purkinje images of two LEDs, and no any user-dependent parameters are used in our paper, which require a complicated calibration procedure for each user. Second, to compute the gaze point on screen by intersecting the estimated 3D gaze, the positions of LEDs and the monitor need to be calibrated in advance. By introducing a planar mirror, two LEDs and the monitor are calibrated using a simple method without any other devices. Experimental results show that our gaze tracking system achieves an average estimation error of about 3 degree. © 2010 IEEE.

关键词： Human computer interaction

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The depth information estimation of microscope defocus image based-on Markov random field

The depth information estimation of microscope defocus image...

引用

2008 IEEE International Conference on Robotics, Automation and Mechatronics, RAM 2008

作者： Zeng, Xiangjin Huang, Xinhan Wang, Min Peng Li Department of Control Science and Engineering Key Lab. of Image Processing and Intelligence Control HUST Wuhan China

ISBN: (纸本)9781424416769

For depth information estimation of microscope defocus image, a blur parameter model of defocus image based on Markov random field has been present. It converts problem of depth estimation into optimization problem. An improved Iterated Conditional Modes Algorithm has been applied to complete optimization problem, which the select of initial point employed Least squares estimate algorithm prevents that the result gets into local optimization. The experiments and simulations prove that the model and algorithm is efficiency. © 2008 IEEE.

关键词： Microscopes

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Vehicle Type Classification via Adaptive Feature Clustering for Traffic Surveillance Video

Vehicle Type Classification via Adaptive Feature Clustering ...

引用

International Conference on Wireless communications and Signal processing

作者： Shu Wang Feng Liu Zongliang Gan Ziguan Cui Jiangsu Province Key Lab on Image Processing & Image Communications Nanjing University of Posts and Telecommunications

ISBN: (纸本)9781509028610

Vehicle type classification has become an important part of intelligent traffic. However traditional methods can not deal with the varying situations in the reality. In this paper, a novel method is proposed to handle this task in the real road traffic surveillance video. In order to distinguish different vehicles, we categorize vehicles into three types: compact cars, mid-size cars, and heavy-duty vehicles. For a certain video, our method has four steps. First, a deep convolutional neural network is used to detect vehicles in the candidate region and a data set would be generated. Second, the main features of vehicles can be extracted using a fully-connected network. Also, for the sake of higher accuracy, weak labels given by pre-trained extreme learning machine (ELM) are fused into the final features, adding prior information proportionally. Third, K-means is implemented to learn three vehicle-type cluster centers adaptively. Finally, vehicle type will be recognized according to the closest distance principal. Experimental results show that the recognition rate outperforms other traditional methods, verifying the feasibility and effectiveness of the proposed method.

关键词： Vehicle type classification Adaptive clustering Feature learning Deep learning Extreme learning machine

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