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IEEE Conference on Advanced Video and Signal Based Surveillance (AVSS)

作者： Hang Su Hua Yang Shibao Zheng Yawen Fan Sha Wei Institution of Image Communication and Information Processing Department of EE Shanghai Key Laboratory of Digital Media Processing and Transmission Shanghai Jiaotong University Shanghai China

Over the past decades, a wide attention has been paid to crowd control and management in intelligent video surveillance area. In this paper, the authors propose a novel spatiotemporal viscous fluid field to recognize large-scale crowd event with respect to both appearance and driven factor of crowd behavior. Firstly, a spatiotemporal variation matrix is proposed to exploit motion property of a crowd. In particular, the paper exploits characteristics of the matrix with eigenvalue decomposition algorithm and constructs an abstract fluid field to model the crowd motion pattern, which is denoted by spatiotemporal fluid field. Secondly, the paper proposes a spatiotemporal force field to exploit the interaction force between the pedestrians. Furthermore, the fluid and force field constructs a spatiotemporal viscous fluid field. Thirdly, after generating feature with bag of word model, the authors utilize latent Dirichlet allocation model to recognize crowd behavior. The experiments on PETS2009 and UMN datasets show that the proposed method has a better performance for large-scale crowd behavior perception in both robustness and effectiveness comparing with the conventional methods.

关键词： Force Spatiotemporal phenomena Eigenvalues and eigenfunctions Vectors Abstracts Resource management Symmetric matrices

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Collaborative filtering algorithm via compressing the sparse user-rating-data matrix

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Xi'an Dianzi Keji Daxue Xuebao/Journal of Xidian University 2009年第4期36卷 614-618+638页

作者： Hou, Cui-Qin Jiao, Li-Cheng Zhang, Wen-Ge Ministry of Education Key Lab. of Intelligent Perception and Image Understanding Research Inst. of Intelligent Information Processing Xidian Univ. Xi'an 710071 China

The paper proposes a novel memory-based collaborative filtering algorithm-Multi-label Probabilistic Latent Semantic Analysis based Collaborative Filtering, which improves the quality of recommendations by reducing the dimension of the user-rating-data matrix by multi-label probabilistic latent semantic analysis when the matrix is extremely sparse. Firstly, it confines the set of latent variables of probability latent semantic analysis to the set of multi-label of items to make latent variables have meanings of corresponding labels. Then it learns the probabilistic distribution of latent variables, i.e., the model of use's interest, to compress the user-rating-data matrix. Finally, it computes the similarity between different users based on the above learned model and makes recommendations. Compared to memory-based collaborative filtering algorithms, the proposed algorithm decreases the mean absolute error 4 percents averagely on test dataset by reducing the dimension of the user-rating-data matrix. The proposed algorithm makes the recommendation system understandable and obtains competitive recommendations compared to the filtering algorithm which reduces the dimension of the user-rating-data matrix by probabilistic latent semantic analysis.

关键词： Iterative methods

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Interactive object segmentation using iterative adjustable graph cut

Interactive object segmentation using iterative adjustable g...

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2011 IEEE Visual communications and image processing, VCIP 2011

作者： Shi, Ran Liu, Zhi Xue, Yinzhu Zhang, Xiang School of Communication and Information Engineering Shanghai University Shanghai 200072 China Key Laboratory of Advanced Display and System Application Shanghai University Ministry of Education Shanghai 200072 China Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Shanghai 200240 China

ISBN: (纸本)9781457713200

Interactive object segmentation is widely used for extracting any user-interested objects from natural images. A common problem with many interactive segmentation approaches is that the object segmentation quality is degraded due to inaccurate object/background seeds provided by the user. This paper proposes an iterative adjustable graph cut to efficiently solve this problem. First, object/background seeds are initialized based on the object segmentation result obtained with the user-specified scribbles as the interactive input. Then, an iterative seed adjustment scheme is exploited to correct inaccurate seeds and extract new suitable seeds via graph cut, in which the balancing weight between energy terms are adaptively updated to protect stable seeds and speedup the iteration process. Finally, suitable seeds are obtained and graph cut is used to segment the objects. Experimental results demonstrate the better segmentation performance of our approach even if user provides rather rough seeds. © 2011 IEEE.

关键词： image segmentation

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Transformer-based Lossless Compression of Aurora Spectral images: A Spatial-Temporal-Spectral Joint Approach 17

Transformer-based Lossless Compression of Aurora Spectral Im...

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17th IEEE International Conference on Signal processing, ICSP 2024

作者： Cao, Xinlong Li, Jiaojiao Li, Yuanqing Yan, Jiapeng Zheng, Zhenxing Xi'an University of Posts & Telecommunications School of Communications and Information Engineering School of Artificial Intelligence Xi'an710121 China Xi'an Key Laboratory of Image Processing Technology and Applications for Public Security Xi'an710121 China International Joint Research Center for Wireless Communication and Information Processing Technology of Shaanxi Province Xi'an710121 China

ISBN: (纸本)9798350387384

Aurora spectral image lossless compression has seen significant advancements in recent years. However, most compression algorithms are based on traditional image compression techniques, focusing solely on spectral and spatial correlations without considering temporal correlations. To further enhance compression performance, this paper proposes a Transformer-based point-by-point prediction algorithm for auroral spectral images, which simultaneously uses spatial, spectral and temporal contexts for prediction. Our prediction network consists of two parts: an encoder and a decoder. The encoder is composed of the encoding unit of the original Transformer and is used to extract features from spatial, spectral and temporal contexts. The decoder consists of fully connected layers and is used for prediction. Experimental results show that the average bitrate of this method is reduced by 0.179 bpp compared with the JPEG2000 algorithm, and the average bitrate is reduced by 0.054 bpp compared with the online DPCM algorithm. © 2024 IEEE.

关键词： image compression

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Attention State Assessment by Analyzing Electroencephalograph Signals

Attention State Assessment by Analyzing Electroencephalograp...

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International Congress on image and Signal processing, BioMedical Engineering and Informatics

作者： Junchang Zhao Xiaolin Huang Jun Wang Image Processing and Image Communications Key Lab Nanjing University of Posts & Telecomm Institute of Biomedical Electronic Engineering School of Electronic Science and Engineering Nanjing University

ISBN: (纸本)9781509037117

For brain function study, it is very important to assess the state of different attention conditions. In this paper, we study a cross-correlation between different attention electroencephalograph (EEG) signals by detrended cross-correlation analysis (DCCA). We use the method to study attention α wave EEG. And, we found that is possible to discriminate the cross-correlation between the meditation state of closing eyes and the absence of mind. It was found that the DCCA values of idle subjects' α wave EEG signals increased compared the meditating participants' α wave EEG signals. And it can be particularly helpful for treatment and brain development research.

关键词： meditation activity DCCA a wave

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Geometric Motion Flow (GMF): A New Feature for Traffic Surveillance

Geometric Motion Flow (GMF): A New Feature for Traffic Surve...

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International Conference on image and Graphics (ICIG)

作者： Yawen Fan Hua Yang Shibao Zheng Hang Su Institution of Image Communication and Information Processing Department of EE Shanghai Key Laboratory of Digital Media Processing and Transmission Shanghai Jiaotong University Shanghai China

Motion analysis is still a challenging task in many computer vision applications. This paper proposes a new low-level motion feature based on geometric regularity information, particularly suited for traffic surveillance. Firstly, a novel concept of temporal geometry consistency constraint (TGCC) is introduced, which exploits the fact that the geometric structure of a rigid object remains consistent across consecutive frames. Furthermore, the spatial geometric flow is adopted to characterize image structure. Finally, the video motion is represented as a set of geometric flow that moves in the temporal direction. In this case, the method yields a promising illumination robust moderately dense geometric motion flow (GMF) and has more explicit motion boundaries. The GMF could also be used for higher level motion modeling and structural inference tasks, as an effective low-level feature. Extensive experiment results on real video demonstrate the effectiveness and robustness of the proposed method for vehicle motion analysis.

关键词： Optical imaging Geometrical optics Robustness Lighting Computer vision Motion estimation Geometry

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Signal detection in severely heavy-tailed radar clutter 29

Signal detection in severely heavy-tailed radar clutter

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29th Asilomar Conference on Signals, Systems and Computers, ACSSC 1995

作者： Tsihrintzis, George A. Tsakalides, Panagiotis Nikias, Chrysostomos L. Communication Systems Lab Department of Electrical Engineering University of Virginia CharlottesvilleVA22903-2442 United States Signal and Image Processing Institute Department of Electrical Engineering-Systems University of Southern California Los AngelesCA90089-2564 United States

ISBN: (纸本)0818673702

Alpha-stable distributions have recently been recognized in the signal processing community as simple, yet very accurate, two-parameter statistical models for signals and noises that contain an impulsive component of various degrees of severity. On the basis of this finding, several signal processing problems have been addressed and solved within the framework of alpha-stable distributions and with the use of fractional, lower-order statistics. In this paper, we attempt to popularize these new signal processing tools within the radar community. In particular, we evaluate the goodness-of-fit of alpha-stable models in the radar environment and test the performance of new signal processing algorithms for signal detection and classification on real radar, sea-clutter data. © 1996 IEEE

关键词： Signal detection

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Rapid and multimodal in vivo bioimaging of cancer cells through in situ biosynthesis of Zn＆Fe nanoclusters

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Nano Research 2017年第8期10卷 2626-2632页

作者： Tianyu Du Chunqiu Zhao Fawad ur Rehman Lanmei Lai Xiaoqi Li Yi Sun Shouhua Luo Hui Jiang Matthias Selke Xuemei Wang State Key Laboratory of Bioelectronics (Chien-Shiung Wu Lab) School of Biological Science and Medical Engineering Southeast University Nanjing 210096 China Nanjing Foreign Language School Nanjing 210096 China Laboratory of the signal and image processing School of Biological Science and Medical Engineering Southeast University Nanjing 210096 China Department of Chemistry and Biochemistry California State University Los Angeles CA 90032 USA

Early diagnosis remains highly important for efficient cancer treatment, and hence, there is significant interest in the development of effective imaging strategies. This work reports a new multimodal bioimaging method for accurate and rapid diagnosis of cancer cells by introducing aqueous Fe^2＋ and Zn^2＋ ions into cancer cells （i.e., HeLa, U87, and HepG2 cancer cells）. We found that the biocompatible metal ions Fe^2＋ and Zn^2＋ forced the cancer cells to spontaneously synthesize fluorescent ZnO nanoclusters and magnetic Fe3O4 nanoclusters. These clusters could then be used for multimodal cancer imaging by combining fluorescence imaging with magnetic resonance imaging and computed tomography imaging. Meanwhile, for normal cells （i.e., L02） and tissues, neither fluorescence nor any other obvious difference could be detected between pre- and post-injection. This multimodal bioimaging strategy based on the in situ biosynthesized Zn＆Fe oxide nanoclusters might therefore be useful for early cancer diagnosis and therapy.

关键词： multimodal imaging biosynthesized Zn＆Fe oxide nanoclusters cancer diagnosis

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Early TU Split Termination in HEVC Based on Quasi-Zero-Block

Early TU Split Termination in HEVC Based on Quasi-Zero-Block

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2013 3rd International Conference on Electric and Electronics(EEIC 2013)

作者： Yunyu Shi Zhiyong Gao Xiaoyun Zhang Institute of Image Communication and Network Engineering& Shanghai Key Laboratory of Digital Media Processing and Transmissions Shanghai Jiaotong University

The new introduced concept, transform tree or residual quadtree(RQT) in HEVC standard, brings high coding performance along with high computational complexity because the optimal TU partition is estimated in a ratedistortion sense at the encoder sides by testing all kinds of partitions. This paper focus on early TU split termination in terms of the tradeoff between computation and coding quality. An early TU split termination scheme based on quasi-zeroblock(QZB) is proposed, which is defined by two aspects about the quantized transform coefficients;the sum of all absolute coefficients and the number of nonzero coefficients. Besides, the selective probability of every TU depth is calculated and analyzed. Experimental results show that the proposed method(HM-QZB) could achieve 22.8% reduction in encoding time and 50.59% reduction in TU processing time compared to the HEVC test model HM10.0 encoder with about 0.04 d B BDPSNR loss in coding performance.

关键词： HEVC early termination TU split residual quadtree(RQT) quasi-zero-block(QZB)

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An Improved Outdoor Camera Calibration Method Based On EKF

An Improved Outdoor Camera Calibration Method Based On EKF

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2014 International Conference on Computer,communications and Information Technology(CCIT 2014)

作者： Shaopo Zhu Dong Hu Education Ministry's Key Lab of Broadband Wireless Communication and Sensor Network Technology Jiangsu Province's Key Lab of Image Procession and Image Communications Nanjing University of Posts and Telecommunications

ISBN: (纸本)9781629939865

An improved outdoor camera calibration method based on Extended Kalman Filter(EKF) is proposed in this paper. GPS coordinate system is introduced to establish world coordinate system in the calibration procedure. According to the characteristics of GPS coordinate converted to plane coordinate and the camera calibration model, we establish the mapping relations of reference points between image plane coordinate and the world coordinate. Further, we consider the world coordinate error, set the world coordinates as the measure quantities, use the method of EKF to get the optimal estimation of intrinsic and extrinsic camera parameters, in EKF the camera's intrinsic and extrinsic parameters are set as state variables, the transformation equations of image coordinate to the world coordinate are set as the measurement equations, and we let the observed feature points' image coordinates and corresponding world coordinates as the input of the filter. Experimental results confirmed that the proposed method with high precision.

关键词： camera calibration,GPS coordinate,EKF,world coordinate error

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