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International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC

作者： Ling Li Hua Yang Hang Su Yihua Cao Shibao Zheng Institution of Image Communication and Information Processing Department of EE Shanghai Key Laboratory of Digital Media Processing and Transmission Shanghai Jiao Tong University Shanghai China

With the accelerated global urbanization and life rhythm, an increasing physical and mental stress has been imposed, which led to a variety of problems that could not be ignored. This paper proposes a novel web-based telemedicine system that is focusing on psychotherapy. Considering the requirement of the psychological treatment, the function of system contains high definition video monitoring, two-way voice talkback, physiology parameters monitoring and data management based on database. The solution of the system is presented in the paper, including the hardware and software design. Some key issues in the system, including HD video processing, physiological parameters processing, are also discussed. Finally, the demonstration system and performance evaluation are presented.

关键词： Medical services Biomedical monitoring High definition video Servers Telemedicine Databases Decoding

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Coarse carrier and timing synchronization for interleaved OFDMA uplink system

Coarse carrier and timing synchronization for interleaved OF...

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IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)

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

ISBN: (纸本)9781424444618

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： Timing Delay estimation Delay effects Frequency conversion Frequency estimation Frequency synchronization Time domain analysis Autocorrelation Extraterrestrial measurements Multipath channels

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Attentive Semantic Exploring for Manipulated Face Detection

Attentive Semantic Exploring for Manipulated Face Detection

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IEEE International Conference on Acoustics, Speech and Signal processing

作者： Zehao Chen Hua Yang Institute of Image Communication and Network Engineering Shanghai Jiao Tong University Shanghai China Shanghai Key Lab of Digital Media Processing and Transmission Shanghai China

Face manipulation methods develop rapidly in recent years, whose potential risk to society accounts for the emerging of researches on detection methods. However, due to the diversity of manipulation methods and the high quality of fake images, detection methods suffer from a lack of generalization ability. To solve the problem, we find that segmenting images into semantic fragments could be effective, as discriminative defects and distortions are closely related to such fragments. Besides, to highlight discriminative regions in fragments and to measure contribution to the final prediction of each fragment is efficient for the improvement of generalization ability. Therefore, we propose a novel manipulated face detection method based on Multilevel Facial Semantic Segmentation and Cascade Attention Mechanism. To evaluate our method, we reconstruct two datasets: GGFI and FFMI, and also collect two open-source datasets. Experiments on four datasets verify the advantages of our approach against other state-of-the-arts, especially its generalization ability.

关键词： image segmentation Semantics Diversity methods Feature extraction Face detection Distortion measurement Speech processing

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Vehicle Color Recognition Based on Superpixel Features

Vehicle Color Recognition Based on Superpixel Features

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2019第十一届数字图像处理国际会议

作者： Qiuli Lin Feng Liu Qiang Zhao Ran Xu Jiangsu Province Key Lab on Image Processing & Image Communications Nanjing University of Posts and Telecommunications College of Educational Science and Technology Nanjing University of Posts and Telecommunications Key Lab of Broadband Wireless Communication and Sensor Network Technology Ministry of Education Nanjing University of Posts and Telecommunications

In this paper,a novel methodology is presented to settle the region of interest(ROI) detection problem in vehicle color recognition so as to remove the redundant components of vehicles that interfere greatly with color *** order to make full use of the local color and spatial information,vehicle images are divided into different superpixels at *** spatial relationship between superpixels and the outermost pixels is then used for the background removal of vehicle *** comparing with the vehicle window clustering centroids obtained by k-means,the superpixels close to the universal color characteristics of windows are removed so that the dominant color superpixels are ***,a linear Support Vector Machine classifier is trained for color *** experiments demonstrate that the proposed methodology is effective for color region of interest detection and thus contribute to vehicle color recognition.

关键词： vehicle color recognition region of interest superpixel universal color characteristics Support Vector Machine

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Effective Scheme for Global Abnormal Event Detection for Surveillance Video

Effective Scheme for Global Abnormal Event Detection for Sur...

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2016 International Conference on communications, Information Management and Network Security (CIMNS2016)

作者： Fangxu Dong Dong Hu Education Ministry's Key Lab of Broadband Wireless Communication and Sensor Network Technology Education Ministry's Engineering Research Center of Ubiquitous Network and Heath Service Jiangsu Province's Key Lab of Image Processing and Image Communications Nanjing University of Posts and Telecommunications

ISBN: (纸本)9781510830981

An effective algorithm for global abnormal detection from surveillance video is proposed in this paper. The algorithm is based on sparse representation. To deal with the illumination change in video scenes, specific feature extract methods are designed for corresponding illumination conditions. In the case of non-uniform illumination, features are extracted directly on the original image;in the case of uniform illumination, features are extracted on the binary image obtained by threshold segmentation on the difference image, where the thresholds are computed by the Otsu's method. The features extracted on normal video are used to learn an over-complete dictionary. Then, the sparse reconstruction cost over the dictionary is used to detect abnormal events. Experiments on the open global abnormal dataset and the comparison to the state-of-the-art methods validate effectiveness and quickness of our algorithm.

关键词： abnormal detection illumination change binary images sparse reconstruction cost

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Automatic sign language recognition: Vision based feature extraction and probabilistic recognition scheme from multiple cues 08

Automatic sign language recognition: Vision based feature ex...

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1st International Conference on Pervasive Technologies Related to Assistive Environments, PETRA 2008

作者： Caridakis, George Diamanti, Olga Karpouzis, Kostas Maragos, Petros Image Video and Multimedia Systems Lab. National Technical University of Athens Iroon Polytexneiou 9 15780 Athens Greece Computer Vision Speech Communication and Signal Processing Group National Technical University of Athens Iroon Polytexneiou 9 15780 Athens Greece

ISBN: (纸本)9781605580678

This work focuses on two of the research problems comprising automatic sign language recognition, namely robust computer vision techniques for consistent hand detection and tracking, while preserving the hand shape contour which is useful for extraction of features related to the handshape and a novel classification scheme incorporating Self-organizing maps, Markov chains and Hidden Markov Models. Geodesic Active Contours enhanced with skin color and motion information are employed for the hand detection and the extraction of the hand silhouette, while features extracted describe hand trajectory, region and shape. Extracted features are used as input to separate classifiers, forming a robust and adaptive architecture whose main contribution is the optimal utilization of the neighboring characteristic of the SOM during the decoding stage of the Markov chain, representing the sign class. Copyright 2008 ACM.

关键词： image segmentation

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Hessian based image structure adaptive gradient vector flow for parametric active contours

Hessian based image structure adaptive gradient vector flow ...

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2010 17th IEEE International Conference on image processing, ICIP 2010

作者： Wang, Y.Q. Chen, W.F. Yu, T.L. Zhang, Y.T. Tianjin Key Lab. of Intelligent Computing and Novel Software Technology Tianjin University of Technology Tianjin 300191 China Key Lab. of Image Processing Southern Medical University Guangzhou 510515 China Dept. of Radiology General Hospital Tianjin Medical University Tianjin 300052 China

ISBN: (纸本)9781424479948

Active contours have been one of the most successful methods for image segmentation during the last two decades, but one of the shortcomings of being unable to converge to concavity is a handicap to its effectiveness. In order to address this issue, the gradient vector flow (GVF) was put forth. Although there have been a great number of works on GVF, the image structure has seldom been incorporated into GVF algorithm. In this work, the image structure characterized by the Hessian matrix is incorporated into the GVF algorithm by reformulating the smoothness constraint of GVF into matrix form. In this way, the associated diffusion PDEs are anisotropic and the modified GVF snake can converge to very long concavity and preserve weak edge simultaneously. Experiments and comparisons are presented to demonstrate the properties of the proposed strategies. © 2010 IEEE.

关键词： image segmentation

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Remote sensing image compression for deep space based on region of interest

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Journal of Harbin Institute of Technology(New Series) 2003年第3期10卷 300-303页

作者：王振华吴伟仁田玉龙田金文柳健 Institute for Pattern Recognition and Artificial Intelligence State Key Lab for Image Processing and Intelligent ControlHuazhong University of Science and Technology Wuhan 430074 China Institute for Pattern Recognition and Artificial Intelligence State Key Lab for Image Processing and Intelligent ControlHuazhong University of Science and Technology Wuhan 430074 China major limitation for deep space communication is the limited bandwidths available. The downlink rate using X-band with an L2 halo orbit is estimated to be of only 5.35 GB/d. However the Next Generation Space Telescope (NGST) will produce about 600 GB/d. Clearly the volume of data to downlink must be reduced by at least a factor of 100. One of the resolutions is to encode the data using very low bit rate image compression techniques. An very low bit rate image compression method based on region of interest(ROI) has been proposed for deep space image. The conventional image compression algorithms which encode the original data without any data analysis can maintain very good details and haven't high compression rate while the modern image compressions with semantic organization can have high compression rate even to be hundred and can't maintain too much details. The algorithms based on region of interest inheriting from the two previews algorithms have good semantic features and high fidelity and is therefore suitable for applications at a low bit rate. The proposed method extracts the region of interest by texture analysis after wavelet transform and gains optimal local quality with bit rate control. The Result shows that our method can maintain more details in ROI than general image compression algorithm(SPIHT) under the condition of sacrificing the quality of other uninterested areas

A major limitation for deep space communication is the limited bandwidths available. The downlinkrate using X-band with an L2 halo orbit is estimated to be of only 5.35 GB/d. However, the Next GenerationSpace Telescope (NGST) will produce about 600 GB/d. Clearly the volume of data to downlink must be re-duced by at least a factor of 100. One of the resolutions is to encode the data using very low bit rate image com-pression techniques. An very low bit rate image compression method based on region of interest(ROI) has beenproposed for deep space image. The conventional image compression algorithms which encode the original datawithout any data analysis can maintain very good details and haven' t high compression rate while the modernimage compressions with semantic organization can have high compression rate even to be hundred and can' tmaintain too much details. The algorithms based on region of interest inheriting from the two previews algorithmshave good semantic features and high fidelity, and is therefore suitable for applications at a low bit rate. Theproposed method extracts the region of interest by texture analysis after wavelet transform and gains optimal localquality with bit rate control. The Result shows that our method can maintain more details in ROI than generalimage compression algorithm(SPIHT) under the condition of sacrificing the quality of other uninterested areas.

关键词： wavelet compression ROI deep space

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LS-SVM based dim and small infrared target dualband fusion detection

LS-SVM based dim and small infrared target dualband fusion d...

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2nd International Conference on Space Information Technology

作者： Sun Yuqiu Li Shalan Tian Jinwen Liu Jian Yangtze Univ Sch Informat & Math Jinzhou 434023 Hubei Peoples R China Huazhong Univ Sci & Technol Image Processing & Intelligence Control Key Lab State Educ Minist Inst Pattern Recognition & Artificial Intelligece Wuhan 430074 Peoples R China

ISBN: (纸本)9780819469601

Aiming at the characters of weak and small targets in infrared images, an algorithm based on Least Squares Support Vector Machines (LS-SVM) is presented to fuse long-wave and mid-wave infrared images and detect targets. image intensity surfaces for the neighborhood of every pixel of the original long-wave infrared image and mid-wave infrared are well-fitted by mapped LS-SVM respectively. And long-wave and mid-wave infrared image gradient images are obtained by LS-SVM based on radial basis kernels function. Fusion rule is set up according to the features of gradient images. At last, segment fused image and targets can be detected with contrast threshold. Compared with wavelet fusion detection algorithm and morphological fusion detection algorithm, when a target is affected by baits, the experimental results demonstrate that the proposed approach in the paper based on LS-SVM to fuse and detect weak and small target is reliable and efficient.

关键词： least squares support vector infrared image fusion detection

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Computer Aided Diagnosis of Fatty Liver Ultrasonic images Based on Support Vector Machine

Computer Aided Diagnosis of Fatty Liver Ultrasonic Images Ba...

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30th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society

作者： Li, Guokuan Luo, Yu Deng, Wei Xu, Xiangyang Liu, Aihua Song, Enmin Huazhong Univ Sci & Technol Sch Comp Sci & Technol Wuhan 430074 Peoples R China Minist Educ Key Lab Image Processing & Intelligent Control Wuhan 430074 Peoples R China Sixth Hosp Ultrasound Dept Wuhan Peoples R China

ISBN: (纸本)9781424418145

B-scan ultrasound is the primary means for the diagnosis of fatty liver. However, due to use of various ultrasound equipments, poor quality of ultrasonic images and physical differences of patients, fatty liver diagnosis is mainly qualitative, and often depends on the subjective judgment of technicians and doctors. Therefore, computer-aided feature extraction and quantitative analysis of liver B-scan ultrasonic images will help to improve clinical diagnostic accuracy, repeatability and efficiency, and could provide a measure for severity of hepatic steatosis. This paper proposed a novel method of fatty liver diagnosis based on liver B-mode ultrasonic images using support vector machine (SVM). Fatty liver diagnosis was transformed into a pattern recognition problem of liver ultrasound image features. According to the different characteristics of fatty liver and healthy liver, important image features were extracted and selected to distinguish between the two categories. These features could be represented by near-field light-spot density, near-far-field grayscale ratio, grayscale co-occurrence matrix, and neighborhood gray-tone difference matrix (NGTDM). A SVM classifier was modeled and trained using the clinical ultrasound images of both fatty liver and normal liver. It was then exploited to classify normal and fatty livers, achieving a high recognition rate. The diagnostic results are satisfactorily consistent with those made by doctors. This method could be used for computer-aided diagnosis of fatty liver, and help doctors identify the fatty liver ultrasonic images rapidly, objectively and accurately.

关键词： fatty liver B-scan ultrasound grayscale co-occurrence matrix SVM feature extraction

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