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IEEE International Symposium on Circuits and Systems

作者： Xiongkuo Min Guangtao Zhai Zhongpai Gao Ke Gu Institute of Image Communication and Information Processing Shanghai Jiao Tong University Shanghai China Shanghai Key Laboratory of Digital Media Processing and Transmissions

ISBN: (纸本)9781479934331

images usually contain areas that particularly attract people's attention and visual attention is an important feature of human visual system (HVS). Visual attention had been shown to be effective in improving performance of existing image quality assessment (IQA) metrics. However, with the quick advancement of IQA research, the booming of open IQA databases calls for associated comprehensive and accurate visual attention dataset. Despite of the large number of existing computational attention/saliency models, the most accurate measure of human attention is still human based. In this research, we first conduct extensive eye tracking experiments for all the pristine images from the seven widely used IQA databases (LIVE, TID2008, CSIQ, Toyama, LIVE Multiply Distortion, IVC and A57 databases). Then we propose a gaze-duration adaptive weighting approach to generate saliency maps from the eye tracking data. When applied on the IQA databases, experimental results suggest that accuracy of benchmark quality metrics, e.g. PSNR and SSIM can be systematically improved, outperforming existing saliency datasets. Both the eye tracking data and the saliency maps in this research will be made publicly available at ***.

关键词： important HVS IQA

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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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Co-cyclic jacket matrix based optimal training design and placement for MIMO OFDM channel estimation

Co-cyclic jacket matrix based optimal training design and pl...

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2008 4th IEEE International Conference on Circuits and Systems for communications, ICCSC

作者： Lin, Chengyu Zhang, Wenjun Guo, Ying Xu, Youyun 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 School of Information Science and Engineering Central South University Changsha 410083 China

ISBN: (纸本)9781424417087

This paper deals with optimal training design and placement over multiple OFDM symbols for the least squares (LS) channel estimation in multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. When the number of transmit antennas increases, more pilot tones are required and the transmission efficiency decreases. To solve this problem, the pilot sequences are distributed over multiple OFDM symbols. Motivated by the properties of co-cyclic Jacket matrices, we derive the optimal pilot sequences from co-cyclic Jacket matrices based on the minimum mean square error (MSE) of the LS channel estimation. Simulation results show that the derived optimal pilot sequences over one, two and four OFDM symbols have the same performance, and greatly outperform the orthogonal pilot sequences in slowly time-varying channels. ©2008 IEEE.

关键词： Orthogonal frequency division multiplexing

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The large-scale crowd density estimation based on sparse spatiotemporal local binary pattern

The large-scale crowd density estimation based on sparse spa...

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IEEE International Conference on Multimedia and Expo (ICME)

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

ISBN: (纸本)9781612843483

Over the past decade, a wide attention has been paid to the crowd control and management in intelligent video surveillance area. This paper proposes a sparse spatiotemporal local binary pattern (SST-LBP) descriptor to extract the dynamic texture of the walking crowd with the application to crowd density estimation. Firstly, the sparse selected location is extracted, which is notably variant in temporal domain and scale invariant in spatial domain. Afterwards, considering the spatial and temporal symmetry, the authors propose a sparse spatiotemporal local binary pattern algorithm and utilize its statistical property to describe the crowd feature. Finally, the crowd features are classified into a range of density levels by adopting support vector machine. The experiments on real video show that the proposed SST-LBP method is effective and robust on the large-scale crowd density estimation. Compared with the other methods, the proposed method does not base on the premise that the background should be extracted perfectly, which is too complicated to implement in real surveillance.

关键词： Spatiotemporal phenomena Feature extraction Estimation Support vector machines Heuristic algorithms Legged locomotion Equations

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The analysis of biological cell behaviors using Bayesian bidirectional network model

The analysis of biological cell behaviors using Bayesian bid...

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International Conference on BioMedical Engineering and Informatics (BMEI)

作者： Keming Zhang Hua Yang Chongyang Zhang Shanghai Key Laboratory of Digital Media Processing and Transmission Department of EEInstitution of Image Communication and Information Processing Shanghai Jiaotong University Shanghai China

As the increasing demand on medical and biological research, automatic behavior analysis for large scale biological cells faces a big challenge. A novel Bayesian bidirectional network model is proposed in this paper for detection and statistic of the cells behaviors such as birth, vanishing, split, merging and so on. First the cells in every frame are distinguished from background by mean shift filter and level set segmentation. Then we build a bidirectional Bayesian network by modeling the cell regions as vertices and the similarity of cell regions in successive frames as directed edges. By applying weights on the directed edges, the graph model represents the relation between cell regions. Finally, in order to remove fake edges, a iterative algorithm is adopted for analyzing the graph model and obtaining the optimal path of each cell. Experiment result shows that the proposed method is effective and accurate to detect most cells behaviors.

关键词： Bayesian methods image segmentation Level set Feature extraction Biomedical imaging Tracking image edge detection

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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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Crowd Event Perception Based on Spatio-temporal Viscous Fluid Field

Crowd Event Perception Based on Spatio-temporal Viscous Flui...

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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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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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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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Make your choice for multimodal knowledge graph completion

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Knowledge-Based Systems 2025年 323卷

作者： Yifan Xue Shuoyan Ren Wanqiang Cai Yingyao Ma Lotfi Senhadji Huazhong Shu Jiasong Wu Laboratory of Image Science and Technology Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Ministry of Education Nanjing 210096 China Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing Southeast University Nanjing 210096 China School of Artificial Intelligence and Computer Science Jiangnan University Wuxi 214122 China Univ Rennes INSERM LTSI - UMR 1099 F-35000 Rennes France Centre de Recherche en Information Biomédicale Sino-français (CRIBs) Univ Rennes INSERM F-35000 Rennes France

Knowledge graph completion (KGC) aims to predict missing entities in knowledge graphs by learning effective representations of entities and their relations. Recent advances have explored multimodal KGC by incorporating structural, textual, and visual information. However, two critical challenges remain unresolved: (1) modal heterogeneity, where significant differences in feature distributions across modalities hinder effective fusion; and (2) spatial heterogeneity, where embedding knowledge graphs in a single geometric space fails to capture their complex topological structures. To address these challenges, we propose ChoicE, a unified framework that leverages a mixture of experts (MoE) design for both encoding and decoding. In the encoder, the multimodal chooser preprocesses multiple modalities to derive embedding representations for each modality. These representations are then processed by distinct experts specialized for structural, textual, and visual features, facilitating effective fusion while preserving modality-specific information. In the decoder, the geometric chooser projects the unified multimodal embeddings into Euclidean, complex, or hyperbolic space, dynamically selecting the most appropriate space to model the inference patterns inherent to each query. Extensive experiments on multiple benchmark datasets demonstrate that ChoicE effectively overcomes these dilemmas and achieves state-of-the-art performance in multimodal KGC. The data and code are released at https://***/r/ChiocE-master/ .

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