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作者： Hu, D. Hu, J. Education Ministry's Key Lab of Broadband Wireless Communication and Sensor Network Technology Nanjing University of Posts and Telecommunications China Jiangsu Province's Key Lab of Image Procession and Image Communications Nanjing University of Posts and Telecommunications China

ISBN: (纸本)9781845648282

Nonparametric model is one of the popular ones for background modeling for its ability to adapt to changes quickly in dynamic environment and enable very sensitive detection of moving objects. However, the method is too complex and computationally inefficient to real-time application. In this paper, a fast and efficient scheme for moving object detection by nonparametric background modeling was proposed, whereas the background was modeled combining clustering theory to eliminate the large amount of redundant information and noise pixels in background modeling stage. Moreover, adaptive background and temporal differencing was used to filter out unchanged background pixels, thus enabling an additional reduction in computational requirements and improving the detection results. Besides, RGB color components were substituted by chromaticity coordinates to suppress shadow effects. Experimental results demonstrated the effectiveness of the proposed algorithm. © 2014 WIT Press.

关键词： Object detection

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Symbolic transfer entropy analyzed sleep electroencephalogram

Symbolic transfer entropy analyzed sleep electroencephalogra...

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2013 International Conference on Information,Business and Education Technology(ICIBET 2013)

作者： Ruijun Chang Liu Yang Qiuping Chen Jun Wang Image Processing and Image Communications Key Lab College of Geo & Bio InformationNanjing Univ of Posts & Telecomm

ISBN: (纸本)9781629932101

The quality of sleep has a great relationship with health. The result of sleep stage classification is an important indicator to measure the quality of sleep. It was found that the symbolic transfer entropy about the α wave of the wake and the first stage of non-rapid eye movement sleep reflect on the changes of sleep stage. And it was confirmed by T test and multisamples experiments. The symbolic transfer entropy can apply into automatic sleep stage classification. By Multi-parameter analysis it could achieve a higher accuracy of sleep stage classification.

关键词： EEG symbolic transfer entropy sleep stage classification, α wave

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Pathological Electrocardiogram Analysis based on Symbolic Relative Entropy

Pathological Electrocardiogram Analysis based on Symbolic Re...

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2013 International Conference on Bio-Medical Materials and Engineering(ICBME 2013)

作者： Jiakai Qiu Xiaoliang Huang Suyi Chang Yunxia Wang Jun Wang Image Processing and Image Communications Key Lab College of Geo & Bio InformationNanjing Univ of Posts & Telecomm

In this paper, symbolic relative entropy was used to analyze normal electrocardiogram(ECG), the ECG taken from patient with congestive heart failure(CHF) and Atrial fibrillation(AF) Statistical testing showed that the symbolic relative entropy of normal ECG was distinctly higher than that of CHF while the symbolic relative entropy of CHF was distinctly higher than that of AF It discoved that symbolic relative entropy can be used to analyze the different pathological ECG which could be used to assisted clinical diagnosis

关键词： Symbolic the Relative Entropy Electrocardiogram

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Research on moving object detection based on the Bayesian theory

Research on moving object detection based on the Bayesian th...

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作者： Hu, D. Liu, X. Education Ministry's Key Lab of Broadband Wireless Communication and Sensor Network Technology Nanjing University of Posts and Telecommunications China Jiangsu Province's Key Lab of Image Procession and Image Communications Nanjing University of Posts and Telecommunications China

ISBN: (纸本)9781845648282

For effective and efficient detection of moving objects from complex surveillance scenarios, a novel integrated object detection scheme based on clustering and Bayesian theory is proposed in this paper. The algorithm conducts clustering analysis in the time domain as the pre-segmentation step for later Bayesian judgment processing. Furthermore, temporal differential image was used to classify dynamical and static pixels, which were then used to direct the Bayesian judgment with proper models. The clustering processing reduced the number of the mixture models, which in turn, reduced the computational complexity while ensuring accurate moving object detection. Simulation results show that the proposed scheme well exploited the temporal correlation of the video sequences and the accuracy of the detected objects can be improved by nearly 20% compared to the typical Gaussian mixture modeling algorithm. © 2014 WIT Press.

关键词： Object detection

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Network flow based collective behavior analysis

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2013 International Workshop on Behavior and Social Informatics and Computing, BSIC 2013

作者： Hu, Yuting Xie, Rong Zhang, Wenjun Institute of Image Communication and Network Engineering Shanghai Key Lab. of Digital Media Processing and Transmission Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9783319040479

With the large-scale activities increasing gradually, the intelligent video surveillance system becomes more and more popular and important. The trajectory identification and behavior analysis are very important techniques for the intelligent video surveillance system. This paper focuses on the trajectory identification and behavior analysis framework for video surveillance system. The framework is implemented on footbridge video and queuing video of Shanghai World Expo 2010 video surveillance system. The experimental results show the efficiency of our proposed framework. © Springer International Publishing Switzerland 2013.

关键词： Integer programming

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Super-resolution based on two dictionary-pairs

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Sensors and Transducers 2013年第10期157卷 289-294页

作者： Li, Xin Zhu, Xiuchang Jiangsu Province Key Lab on Image Processing and Image Communication Nanjing University of Posts and Telecommunications Nanjing 210003 China

For learning-based super-resolution reconstruction, the selection and training of dictionary play an important role in improving image reconstruction quality. A super-resolution algorithm based on two dictionary-pairs is proposed in this paper. This algorithm selects image's high- and mid-frequency components as the features of high- and low-resolution patches respectively, and gets the first dictionary-pair, i.e. joint-basic high/low-resolution dictionary-pair, by means of joint training. Then it calculates the difference between original high/mid-frequency components and reconstructed high/mid-frequency components with the first dictionary-pair, and composes the second dictionary-pair, i.e. residual high- and low-resolution dictionary-pair. During super-resolution reconstruction, the high-frequency component and residual high-frequency component of low-resolution image are reconstructed respectively with the above two dictionary-pairs. The experiment results show that, the subjective and objective reconstruction quality could be effectively improved by our proposed algorithm. © 2013 IFSA.

关键词： image reconstruction

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An efficient algorithm for feature selection with feature correlation

An efficient algorithm for feature selection with feature co...

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3rd Sino-Foreign-Interchange Workshop on Intelligent Science and Intelligent Data Engineering, IScIDE 2012

作者： Huang, Li-Li Tang, Jin Chen, Si-Bao Ding, Chris Luo, Bin School of Computer Science and Technology Anhui University 230601 China Key Lab of Industrial Image Processing and Analysis of Anhui Province Hefei 230039 Anhui China

ISBN: (纸本)9783642366680

Feature selection is an important component of many machine learning applications. In this paper, we propose a new robust feature selection method for multi-class multi-label learning. In particular, feature correlation is added into the sparse learning of feature selection so that we can learn the feature correlation and do feature selection simultaneously. An efficient algorithm is introduced with rapid convergence. Our regression based objective makes the feature selection process more efficient. Experiments on benchmark data sets illustrate that the proposed method outperforms many state-of-the-art feature selection methods. © Springer-Verlag 2013.

关键词： Feature Selection

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image representation and recognition based on directed complex network model

Image representation and recognition based on directed compl...

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8th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2013

作者： Chen, Ying Tang, Jin Luo, Bin School of Computer Science and Technology Anhui University Hefei230601 China Key Lab of Industrial Image Processing and Analysis of Anhui Province Hefei230039 China

ISBN: (纸本)9783642375019

image structure representation is a vital technique in the image recognition. A novel image representation and recognition method based on directed complex network is proposed in this paper. Firstly, the key points are extracted from an image as the nodes to construct an initial complete undirected complex network. Then, the k-nearest neighbor evolution method is designed to form a series of directed networks. At last, the feature descriptor of the image is constructed by concatenating the structure features of each directed network to finally achieve image recognition. Experimental results demonstrate that the proposed method outperforms the traditional methods in image recognition and can describe the structure of images more effectively. © Springer-Verlag Berlin Heidelberg 2013.

关键词： image recognition

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A probability-based object tracking method

A probability-based object tracking method

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4th International Conference on Intelligence Science and Big Data Engineering, IScIDE 2013

作者： Song, Xu Li, Guoqiang Li, Ying Zhang, Yanning Shaanxi Province Key Lab. of Speech and Image Information Processing Northwestern Polytechnical University Xi'an 710129 China Science and Technology on Electro-optic Control Laboratory Louyang 471009 China

ISBN: (纸本)9783642420566

Here we take advantage of the signal recovery power of Compressive Sensing (CS) to significantly reduce the computational complexity brought by the high-dimension image data, then an effective and efficient low-dimensional subspace representation of the object is computing by applying Principal Component Analysis (PCA) to a collection of object observations which are low-dimensional vectors derived from CS. An incremental PCA algorithm is used to update this subspace model for characterizing the object appearance changes. Meanwhile, two distances derived from Probabilistic Principal Component Analysis (PPCA): distance from feature space (DFFS) and distance in feature space (DIFS), are used to describe visual similarity between the learned subspace representation model and candidate targets. Comparing with the traditional used reconstruction error, the sum of two distances: DFFS + DIFS, is more accurate and more robust to noises and partial occlusions. Numerous experiment demonstrate that subspace representation model can handle the situation that target objects experience pose changes, scale changes, significant illumination variation, partial occlusions and so on. © 2013 Springer-Verlag Berlin Heidelberg.

关键词： Compressed sensing

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The complexity of occupational stress electroencephalogram

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Occupational Diseases and Environmental Medicine 2013年第1期1卷 1-3页

作者： Honger Tian Lili Cao Jun Wang Tian Xu Yongguo Zhan Ling Liu Image Processing and Image Communications Key Lab College of Geo & Bio Information Nanjing University of Posts & Tele- comm Nanjing China Key Laboratory of Environment Medicine and Engineering Ministry of Education School of Public Health Southeast University Nanjing China

It is an important method for using electroencephalogram (EEG) to detect and diagnose occupational Stress in clinical practice. In this paper, the complexity analysis method based on Jensen-Shannon Divergence was used to calculate the complexity of occupational stress electroencephalogram from students and *** study found that the complexity of nurses’ EEG was higher than that of students’ EEG. The result can be used to assisted clinical diagnosis.

关键词： Occupational Stress Electroencephalogram Students Nurses Jensen-Shannon Divergence

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