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2010 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting, BMSB 2010

作者： Miao, Ruiqin Sun, Jun Gui, Lin Xiong, Jian Institute of Image Communication and Information Processing Department of Electronic Engineering Shanghai Jiao Tong University Shanghai 200240 China Shanghai Key Laboratory of Digital Media Processing and Transmissions Shanghai Jiao Tong University Shanghai 200240 China

ISBN: (纸本)9781424444625

In orthogonal frequency division multiple access (OFDMA) uplink system, estimations of time delay and carrier frequency offset (CFO) are both particularly challenging. In this paper, a coarse CFO and time delay estimation method is proposed for interleaved OFDMA uplink system. By exploiting the special scheme of interleaved OFDMA, the repetitive time-domain training symbol is designed, and the training sequences for different users have good autocorrelation and cross-correlation properties. Furthermore, the time delay and CFO estimation metrics are presented by making certain correlation between the received training symbol and the local sequence. Through searching peak of the proposed metrics, integral CFO of many subcarriers spaces for each user can be estimated, and then timing delay for each user can be found. Simulations illustrate the proposed estimation scheme can get good performance under multipath channel.

关键词： Frequency division multiple access

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image matching using mutual k-nearest neighbor graph

Image matching using mutual k-nearest neighbor graph

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International Conference of Young Computer Scientists, Engineers and Educators, ICYCSEE 2015

作者： Li, Ting-Ting Jiang, Bo Tu, Zheng-Zheng Luo, Bin Tang, Jin 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)9783662462478

Though weighted voting matching is one of most successful image matching methods, each candidate correspondence receives voting score from all other candidates, which can not apparently distinguish correct matches and incorrect matches using voting scores. In this paper, a new image matching method based on mutual k-nearest neighbor (k-nn) graph is proposed. Firstly, the mutual k-nn graph is constructed according to similarity between candidate correspondences. Then, each candidate only receives voting score from its mutual k nearest neighbors. Finally, based on voting scores, the matching correspondences are computed by a greedy ranking technique. Experimental results demonstrate the effectiveness of the proposed method. © Springer-Verlag Berlin Heidelberg 2015.

关键词： image matching

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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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Vehicle Type Classification via Adaptive Feature Clustering for Traffic Surveillance Video

Vehicle Type Classification via Adaptive Feature Clustering ...

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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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A source-driven error recovery scheme using Wyner-Ziv coding

A source-driven error recovery scheme using Wyner-Ziv coding

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IEEE International Conference onMultimedia and Expo, ICME 2007

作者： Yongsheng, Zhang Hongkai, Xiong Songyu, Yu Hui, Lv Institute of Image Communication and Information Processing Department of Electronic Engineering Shanghai Jiao Tong University Shanghai 200240 China Shanghai Key Laboratory of Digital Media Processing and Transmissions Shanghai Jiao Tong University Shanghai 200240 China

ISBN: (纸本)1424410177

In this paper, we propose an error recovery scheme to cope with the frame loss problem in large end-to-end delay scenario. Because Wyner-Ziv coding can produce deterministic output with nondeterministic inputs, we adopt the decoder's error-corrupted reference picture as side information to exploit its correlation with source picture, and thus improve the coding efficiency. To prevent retransmission request, we propose a feasible encoder-driven rate estimation scheme by only storing MVs and part of the error pattern at encoder. Furthermore, a new Laplacian parameter computing method is proposed based on discrete Laplacian PDF. The experimental results show that the proposed estimation schemes have quite high precision, and the error recovery scheme outperforms INTRA refresh scheme up to 5 dB. © 2007 IEEE.

关键词： Errors

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A novel interference cancellation approach for interleaved OFDMA uplink system

A novel interference cancellation approach for interleaved O...

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In this paper, the issue of carrier frequency offset (CFO) compensation in interleaved orthogonal frequency division multiple access (OFDMA) uplink system is investigated. To mitigate the effect of multiple access interference (MAI) caused by CFOs of different users, a new parallel interference cancellation (PIC) compensation algorithm is proposed. This scheme uses minimum mean square error (MMSE) criterion to obtain the estimation of interference users, then circular convolutions [6] are employed to restore MAI and compensate CFO. To tackle the complexity problem of circular convolutions, an efficient MAI restoration and cancellation method is developed. Simulations illustrate the good performance and low computational complexity of the proposed algorithm. © 2009 The Institute of Electronics, Information and communication Engineers.

关键词： Mean square error

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Adaptive dictionary learning for distributed compressive video sensing

International Journal of Digital Content Technology and its ...

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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

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Object Extraction from Random Background using Stereo Matching 14

Object Extraction from Random Background using Stereo Matchi...

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14th International Conference on Parallel and Distributed Computing Systems, PDCS 2001

作者： Yoo, Chae Gon Lee, Sung Hwan Hwang, Chi Jung Computer & Information Communication Dept. Daeduk College Taejon Korea Republic of Image Processing Lab. Department of Computer Science Chungnam National University Taejon Korea Republic of

ISBN: (纸本)9781618395740

This paper describes a method to extract a pure object image from a complicated background. For example, pure human image can be extracted from random background. This method can be applied to computer vision such as security system, dressing simulation by use of extracted human face, 3D modeling, and robotics. Many researches about stereo matching have been performed. Conventional approaches can be cat++egorized into area-based and feature-based method. In this paper, we start from area-based method and apply boundary tracking using variable size window. Similarity curves will be produced based on window size. Coarse depth information is used for area merging process using similarity curve data. Finally, we produce a pure object image. © (2001) by the International Society for Computers and Their Applications All rights reserved.

关键词： Merging

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Human object classification in daubechies complex wavelet domain 2nd

Human object classification in daubechies complex wavelet do...

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2nd International Conference on Context-Aware Systems and Applications, ICCASA 2013

作者： Khare, Manish Srivastava, Rajneesh Kumar Khare, Ashish Binh, Nguyen Thanh Dien, Tran Anh Image Processing and Computer Vision Lab Department of Electronics and Communication University of Allahabad Allahabad India Ho Chi Minh City University of Technology Ho Chi Minh Viet Nam

ISBN: (纸本)9783319059389

Human object classification is an important problem for smart video surveillance applications. In this paper we have proposed a method for human object classification, which classify the objects into two classes: human and non-human. The proposed method uses Daubechies complex wavelet transform coefficients as a feature of object. Daubechies complex wavelet transform is used due to its better edge representation and approximate shift-invariant property as compared to real valued wavelet transform. We have used Adaboost as a classifier for classification of objects. The proposed method has been tested on standard datasets like, INRIA dataset. Quantitative experimental evaluation results show that the proposed method is better than other state-of-the-art methods and gives better performance. © Springer International Publishing Switzerland 2014.

关键词： Adaptive boosting

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A new medical image segmentation algorithm

A new medical image segmentation algorithm

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2011 International Conference on Computational and Information Sciences, ICCIS 2011

作者： Lu, Yi-Su Chen, Wu-Fan Key Lab. for Medical Image Processing Southern Medical University Guangzhou China Electronic Engineering Department South China Institute of Software Engineering Guangzhou China

ISBN: (纸本)9780769545011

Nonparametric Dirichlet Process Mixtures (MDP) model algorithm is applied to segment images, which can obtain the segmentation class numbers automatically without initialization. In this paper a modified Dirichlet process mixtures model constrained by Markov random field (MRF) prior is constructed, which can smooth MDP image segmentation result and control segmentation classes effectively. Many comparative experiments such as noisy natural images and magnetic resonance images are segmented by classical MDP model and the modified algorithm. The results show the proposed method is robust and accurate. © 2011 IEEE.

关键词： image segmentation

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