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Study of frame-rate up conversion based on H.264

The Journal of China Universities of Posts and Telecommunications 2007年第1期14卷 106-110页

作者： GAN Zong-liang ZHU Xiu-chang Province Key Laboratory on Image Processing and Image Communication Nanjing University of Posts and Telecommunications Nanjing 210003 China

In this study, a low complexity frame-rate up conversion method using compressed domain information for H.264 decoder is proposed. In the proposed scheme, the motion vectors （MVs） are estimated using constant acceleration motion model, and the MVs regarded as no credibility are corrected, and the interpolation method is applied on the basis of the macroblock （MB） coded types. Applied to the H.264 decoder, the proposed method provides high quality interpolation frames and an obvious decrease of the block artifacts.

关键词： video post processing H.264 motion compensated frame interpolation (MCFI) frame-rate up conversion (FRUC)

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Real-Time Student Behavior Analysis via YOLOv5 with Coordinate Attention 9th

Real-Time Student Behavior Analysis via YOLOv5 with Coordina...

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9th International Symposium on Artificial Intelligence and Robotics, ISAIR 2024

作者： Ma, Shuai Chang, Heyou Yang, Xinru Chen, Quan Han, Jian Key Laboratory of Intelligent Information Processing Nanjing XiaoZhuang University Nanjing China School of Information and Engineering Nanjing XiaoZhuang University Nanjing China School of Computer Science Nanjing University of Information Science and Technology Nanjing China Jiangsu Key Laboratory of Image and Video Understanding for Social Safety Nanjing University of Science and Technology Nanjing China Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9789819629138

The detection and recognition of student behavior play a pivotal role in the context of smart classrooms. However, conventional methods often encounter performance degradation due to challenges such as occlusion, data diversity, and complexity. To effectively addresses these issues, we propose a method that integrates YOLOv5 with coordinate attention mechanism and GhostConv for analyzing students' behavior. Specifically, the coordinate attention module enhances the localization ability of the proposed method by addressing problems related to target scale variations and uneven distribution. Additionally, we employ GhostConv to replace conventional convolutional layers in YOLOv5, reducing parameters while improving computational efficiency. To verify the validity of our method, a self-built challenging dataset for real teaching scenarios and Citypersons dataset are selected for the experiment. In comparison to the original YOLOv5, our method has achieved a 14.8% and 5.7% increase in mean average precision rates, with a 19.0% improvement in frames per second. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Students

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Speaker conversion using kernel non-negative matrix factorization

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The Journal of China Universities of Posts and Telecommunications 2017年第5期24卷 60-67页

作者： Xu Qinyu Lu Guanming Yan Jingjie Li Haibo Cheng Xiao College of Telecommunications and Information Engineering Nanjing University of Posts and Telecommunications Nanjing 210003 China Jiangsu Province Key Laboratory on Image Processing and Image Communication Nanjing 210003 China

Voice conversion （VC） based on Gaussian mixture model （GMM） is the most classic and common method which converts the source spectrum to target spectrum. However this method is prone to over-fitting because of its frame-by-frame conversion. The VC with non-negative matrix factorization （NMF） is presented in this paper, which can keep spectrum from over-fitting by adjusting the size of basis vector （dictionary）. In order to realize the non-linear mapping better, kernel NMF （KNMF） is adopted to achieve spectrum mapping. In addition, to increase the accuracy of conversion, KNMF combined with GMM （GKNMF） is also introduced into VC. In the end, KNMF, GKNMF, GMM, principal component regression （PCR）, PCR combined with GMM （GPCR）, partial least square regression （PLSR）, NMF correlation-based frequency warping （NMF-CFW） and deep neural network （DNN） methods are compared with each other. The proposed GKNMF gets better performance in both objective evaluation and subjective evaluation.

关键词： VC kemel NMF spectrum mapping

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Low-light color image enhancement based on NSST

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The Journal of China Universities of Posts and Telecommunications 2019年第5期26卷 41-48页

作者： Wu Xiaochu Tang Guijin Liu Xiaohua Cui Ziguan Luo Suhuai Jiangsu Key Laboratory of Image Processing and Image Communication Nanjing University of Posts and TelecommunicationsNanjing 210003China School of Electrical Engineering and Computing the University of NewcastleNSW 2308Australia

In order to improve the visibility and contrast of low-light images and better preserve the edge and details of images,a new low-light color image enhancement algorithm is proposed in this *** steps of the proposed algorithm are described as ***,the image is converted from the red,green and blue(RGB)color space to the hue,saturation and value(HSV)color space,and the histogram equalization(HE)is performed on the value ***,non-subsampled shearlet transform(NSST)is used on the value component to decompose the image into a low frequency sub-band and several high frequency ***,the low frequency sub-band and high frequency sub-bands are enhanced respectively by Gamma correction and improved guided image filtering(IGIF),and the enhanced value component is formed by inverse NSST ***,the image is converted back to the RGB color space to obtain the enhanced *** results show that the proposed method not only significantly improves the visibility and contrast,but also better preserves the edge and details of images.

关键词： non-subsampled shearlet transform guided image filtering low-light image enhancement the HSV color space

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Human-Centered Financial Signal Analysis Based on Visual Patterns in Stock Charts

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IEEE Transactions on Multimedia 2025年

作者： Luo, Ji-Feng Chen, Yuzhen Zhang, Kaixun An, Xudong Hu, Menghan Zhai, Guangtao Zhang, Xiao-Ping East China Normal University Shanghai Key Laboratory of Multidimensional Information Processing School of Communication and Electronic Engineering Shanghai200241 China University of Chicago Committee on Computational and Applied Mathematics ChicagoIL60637 United States Beijing University of Posts and Telecommunications Beijing100876 China Shanghai Jiao Tong University Institute of Image Communication and Network Engineering Shanghai200240 China Tsinghua Shenzhen International Graduate School Guangdong Shenzhen518055 China

The study utilized a human-centered perspective to research the financial markets, focusing on identifying variations in eye movement patterns between professional and non-professional traders as they analyze a series of stock charts. We use eye movement data as the analysis target because we hypothesize that it represents a behavioral phenotype indicative of stock analysts' cognitive processes during market analysis. Disparities were identified by conducting variance analysis and Wilcoxon signed-rank test on statistical metrics derived from eye fixations and saccades. We provided psychological and economic interpretations to understand the underlying reasons for these observed patterns. To showcase the practical application potential of the human-centered perspective, eye movement data and human visual characteristics were used to construct visual saliency prediction models of professional stock analysts. Leveraging this human-centered model, we developed two practical application demonstrations specifically designed to support and instruct novice traders. Based on the above demonstrations, we designed a training program that demonstrates how, with ongoing training, the non-professional traders' ability to observe stock charts improves progressively. The experimental data and the analyzing code are publicly available at doi: 10.5281/zenodo.10362154. © 1999-2012 IEEE.

关键词： Financial markets

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Free Viewpoint Action Recognition based on Self-similarities

Free Viewpoint Action Recognition based on Self-similarities

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2012 IEEE 11th International Conference on Signal processing(ICSP 2012)

作者： Jiao Wang Changhong Chen Xiuchang Zhu Jiangsu Provincial Key Lab of Image Processing and Image Communication Nanjing University of Posts and Telecommunications

ISBN: (纸本)9781467321969

Action recognition is an important topic in computer vision and most current work focuses on view-dependent representations. In this paper, we develop a novel free viewpoint action recognition based on Self-similarity matrix (SSM), which tends to be stable across views. We choose Local Self-similarity (LSS) descriptor as our low-level feature, then SSM is calculated by computing the similarity between any pair of frame features. Each video sequence is represented using a diagonal descriptor vector extracted from the SSM. Support Vector Machines (SVM) is employed for classification. The encouraging experimental results on the public IXMAS multi-view data set demonstrate effectiveness of the proposed method.

关键词： local self-similarity descriptor self-similarity matrix diagonal feature action recognition view independent

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A trifocal tensor calculation method based on simulated annealing algorithm

A trifocal tensor calculation method based on simulated anne...

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IET International Conference on Information Science and Control Engineering 2012, ICISCE 2012

作者： Li, Jie Liu, Feng Image Processing and Image Communication Jiangsu Key Laboratory Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (纸本)9781849196413

The role of trifocal tensor in three views is similar to basic matrix in two views. It includes projective geometric relations among the three views which do not depend on the structure of the scenery. According to the traditional trifocal tensor calculation. The algorithm precision relies on initial value selection and lacks robustness. The calculation is very complex but the results are not accurate. This paper puts forward a trifocal tensor calculation method based on simulated annealing algorithm. This method overcomes the problem of local minimum and initial value dependence in the majorization process by optimizing the pole coordinates and error through the method of simulated annealing algorithm, and it can get the global optimal solution. Matlab simulation experiment results show that the proposed trifocal tensor calculation method based on simulated annealing algorithm is correct and effective.

关键词： Simulated annealing

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Air Pollution Monitoring By Integrating Local and Global Information in Self-Adaptive Multiscale Transform Domain

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IEEE Transactions on Multimedia 2025年

作者： Gu, Ke Liu, Yuchen Liu, Hongyan Liu, Bo Qiao, Junfei Lin, Weisi Zhang, Wenjun Beijing University of Technology School of Information Science and Technology Engineering Research Center of Intelligent Perception and Autonomous Control of Ministry of Education Beijing Laboratory of Smart Environmental Protection Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing Artificial Intelligence Institute Beijing100124 China Massey University School of Mathematical and Computational Sciences New Zealand Nanyang Technological University College of Computing and Data Science Singapore Shanghai Jiao Tong University Institute of Image Communication and Information Processing Shanghai China

As the deadliest form of pollution, air pollution had a prolonged severe damage to the human health and life safety of nearly 99% of the world's population. Facing to the problem that billions of tons of pollutants discharged into the air per year by industrial activities, this paper proposed a novel image-based air pollution monitor (IAPM) by incorporating local and global information in the self-adaptive multiscale transform domain, so as to achieve the timely and effective leakage detection of typical air pollutants from a single image. To be specific, this paper first developed a screen-shaped module according to two significant findings in visual neuroscience, which include the high sensitivity of human eyes to horizontal and vertical stimuli and the center-surround inhibition, by designing and fusing the square module, horizontal strip module and vertical strip module parallelly for simulating the behaviour of human eyes to extract local features. Second, the learnable weights and proportional mapping were applied to incorporate the screen-shaped module and lightweight vision transformer as backbone, towards more richly exploiting and fusing local and global information just as the way a brain perceives external stimuli. Third, a new self-adaptive multiscale transform domain method was devised based on two motivations from the visual characteristics of multiscale perception and the brain characteristics of self-adaptive domain transform to modify the backbone by using the operations of pooling and pointwise convolution. Extensive experiments implemented on the datasets of carbon particulate matters and ethylene leakage confirmed the superior monitoring performance of the proposed IAPM model beyond the state-of-the-art (SOTA) peers by an accuracy gain of about 4%. Furthermore, the proposed IAPM model only required 0.089 GFLOPs and 0.15 million model parameters, remarkably outperforming SOTA competitors in computational efficiency and storage resources. © 1999-2

关键词： Information leakage

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A Bayesian Super Resolution Algorithm Based on Synthetic Gradient Distribution

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Journal of Donghua University(English Edition) 2011年第3期28卷 305-311页

作者：陈文方向忠 Shanghai Key Laboratory of Digital Media Processing and Transmissions Institute of Image Communication and Information Processing Shanghai Jiaotong University

A novel Bayesian super resolution (SR) algorithm based on the distribution of synthetic gradient is proposed. The synthetic gradient combines prior information in horizontal, vertical, and diagonal directions. Its distribution is modeled as a Lorentzian function and regarded as a new image model which can sufficiently regularize the ill-posed algorithm and preserve the edges in the reconstructed images. The graduated nonconvexity (GNC) optimization is employed to guarantee the convergence of the proposed Lorentzian SR (LSR) algorithm to the global minimum. The performance of LSR is compared with conventional algorithms, and experimental results demonstrate that the proposed algorithm obtains both subjective and objective gains.

关键词： synthetic gradients Lorentzian distribution threshold edge preservation

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Low-light image enhancement based on Retinex theory and dual-tree complex wavelet transform

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Optoelectronics Letters 2018年第6期14卷 470-475页

作者： YANG Mao-xiang TANG Gui-jin LIU Xiao-hua WANG Li-qian CUI Zi-guan LUO Su-huai Jiangsu Key Lab on Image Processing & Image Communication Nanjing University of Posts and Telecommunications School of Electrical Engineering and Computing University of NewcastleNSW 2308 Australia

In order to enhance the contrast of low-light images and reduce noise in them, we propose an image enhancement method based on Retinex theory and dual-tree complex wavelet transform(DT-CWT). The method first converts an image from the RGB color space to the HSV color space and decomposes the V-channel by dual-tree complex wavelet transform. Next, an improved local adaptive tone mapping method is applied to process the low frequency components of the image, and a soft threshold denoising algorithm is used to denoise the high frequency components of the image. Then, the V-channel is rebuilt and the contrast is adjusted using white balance method. Finally, the processed image is converted back into the RGB color space as the enhanced result. Experimental results show that the proposed method can effectively improve the performance in terms of contrast enhancement, noise reduction and color reproduction.

关键词： Retinex theory dual-tree complex wavelet transform image enhancement

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