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

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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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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Improved SAI method using non-local spatial constraint for image interpolation

Improved SAI method using non-local spatial constraint for i...

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2011 International Conference on Wireless communications and Signal processing, WCSP 2011

作者： Bian, Jueping Gan, Zongliang Zhang, Mengcheng Zhu, Xiuchang Jiangsu Province Key Lab on Image Processing and Image Communication NUPT Nanjing China

ISBN: (纸本)9781457710100

This paper proposes an improved image interpolation method based on the soft-decision adaptive interpolation (SAI) algorithm. Natural images often contain repeatable patterns and structures throughout the image, which is called non-local property. We can use this non-local strategy to improve the interpolation quality by better estimating the model parameters and Lagrangian multiplier. There are two steps in our method. In the first step, similar patches of the given block are found in the initialized high resolution image, and the model parameters can be determined properly using the expanded piecewise auto regression (PAR) model and non-local spatial constraint. In the second step, the self-similarity of patches across the high and low resolution images is exploited to solve the Lagrangian multiplier , thus to make the data estimation robust. Experiments indicate that the improved method can achieve good results both subjectively and objectively. © 2011 IEEE.

关键词： Interpolation

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IRSEnet: Differentially Private image Generation with Multi-Scale Feature Extraction and Residual Channel Attention

IRSEnet: Differentially Private Image Generation with Multi-...

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International Conference on Intelligent Control and Information processing (ICICIP)

作者： Jiahao Li Zhongshuai Wang Kamarul Hawari Bin Ghazali Suqing Yan Rushi Lan Xiyan Sun Xiaonan Luo Guangxi Key Lab. of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin China Centre for Advanced Industrial Technology University of Malaysia Pahang Al-Sultan Abdullah Pekan Pahang Malaysia Int. Joint Research Lab. of Spatio-temporal Information and Intelligent Location Services Guilin University of Electronic Technology Guilin China

ISBN: (数字)9798331516147

ISBN: (纸本)9798331516154

Privacy-preserving image generation is particularly crucial in fields like healthcare, where data are both sensitive and limited. However, effective privacy preservation often compromises the visual quality and utility of the generated images due to privacy budget constraints. To address this issue, in this paper, We propose a novel network architecture, IRSEnet, which combines multi-scale feature extraction technology and residual channel attention mechanisms, aiming to enhance the visual quality of generated images and improve the performance of downstream classification tasks under differential privacy. The differential privacy mechanism ensures the security of sensitive data during training, while the multi-scale feature extraction module enhances feature extraction capabilities through parallel convolutional layers at multiple scales. Additionally, the channel attention module dynamically adjusts channel weights to focus on the most discriminative features. Experimental results demonstrate that this model significantly improves the utility of generated images and the accuracy of downstream classification tasks while preserving privacy. Future work will explore the application of this approach on larger datasets and across more diverse tasks.

关键词： Training Differential privacy Privacy Visualization Accuracy image synthesis Computational modeling Feature extraction Security Synthetic data

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Compressed Video Super-resolution Reconstruction Based on Regularized Algorithm

Compressed Video Super-resolution Reconstruction Based on Re...

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2006 8th International Conference on Signal processing

作者： Xu Zhong-qiang Gan Zongliang Zhu Xiu-chang Information Industry Ministry and Jiangsu Province Key Lab of Image Processing & Image Communication Nanjing University of Posts & Telecommunications

ISBN: (纸本)0780397371

Estimating high-resolution(HR) video from a sequence of low-resolution(LR) compressed observations is the focus of this *** on the theory of regularization,this paper proposes a new form of regularized cost function to control the within-channel balance between received data and prior information,and a channel weight coefficient to control the cross-channel *** LR frames are adaptively weighted according to their reliability and the regularization parameter is simultaneously estimated for each channel with ameliorating artifacts in compressed *** iterative gradient descent algorithm is utilized to reconstruction the HR *** results demonstrate that the proposed algorithm has an improvement in terms of both objective and subjective quality.

关键词： Regularization multichannel super-resolution reconstruction compressed video

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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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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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A NOVEL TEMPORAL ERROR CONCEALMENT METHOD BASED ON FUZZY REASONING FOR H.264

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Journal of Electronics(China) 2010年第2期27卷 197-205页

作者： Zhan Xuefeng Zhu Xiuchang Image Processing and Image Communication Lab Nanjing University of Posts and Telecommunications Nanjing 210003 China

In this paper,a fuzzy reasoning based temporal error concealment method is proposed. The basic temporal error concealment is implemented by estimating Motion Vector (MV) of the lost MacroBlock (MB) from its neighboring MVs. Which MV is the most proper one is evaluated by some criteria. Generally,two criteria are widely used,namely Side Match Distortion (SMD) and Sum of Absolute Difference (SAD) of corresponding MV. However,each criterion could only partly describe the status of lost block. To accomplish the judgement more accurately,the two measures are considered together. Thus a refined measure based on fuzzy reasoning is adopted to balance the effects of SMD and SAD. Terms SMD and SAD are regarded as fuzzy input and the term ‘similarity’ as output to complete fuzzy reasoning. Result of fuzzy reasoning represents how the tested MV is similar to the original one. And k-means clustering technique is performed to define the membership function of input fuzzy sets adaptively. According to the experimental results,the concealment based on new measure achieves better performance.

关键词： reasoning Temporal error concealment k-means clustering Adaptive membership function Fhzzy

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Stereo Matching based on Robust Likelihoods and MST Leveraged Smoothness Priors

Stereo Matching based on Robust Likelihoods and MST Leverage...

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

作者： Tianliang Liu Liang Wang Xiuchang Zhu Jiangsu Provincial Key Lab of Image Processing and Image Communication College of Telecommunications and Information Engineering Nanjing University of Posts and Telecommunications

ISBN: (纸本)9781467321969

This paper proposes a global stereo correspondence using robust matching likelihoods and minimum spanning tree （MST） leveraged smooth priors in a probabilistic graphical model framework. The matching likelihoods of the stereo correspondence can be robustly constructed as data term by aggregating initial matching costs from Weber local descriptors using an unsymmetrical guided filtering in a linear model. The disparity priors are devised as smooth term to characterize the smoothness constraints leveraged by the MST structure. The presented stereo approach provides an effective and efficient way to reflect robust visual dissimilarity and resolve local and regional discontinuities. Experiments demonstrate that the proposed global stereo matching method can produce piecewise smooth, accurate and dense disparity map, while removing effectively the visual ambiguity of the stereo matching problem.

关键词： stereo vision Markov random field Weber descriptor minimun spanning tree QPBO optimization

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MOTION VECTOR RECOVERY METHOD BASED ON MEAN SHIFT PROCEDURE

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Journal of Electronics(China) 2010年第6期27卷 830-837页

作者： Zhan Xuefeng Zhu Xiuchang Image Processing and Image Communication Lab Nanjing University of Posts and Telecommunications Nanjing 210003 China

This letter presents a novel Motion Vector (MV) recovery method which is based on Mean Shift (MS) procedure. According to motion continuity, MVs in local area should be similar. If projecting MV into 2-D feature space, local MVs in the feature space tend to cluster closely. To estimate the lost MVs in local area, recovery of lost MVs is modeled as clustering operation. MS procedure is applied to enforce each lost MV in the feature space to shift to the position where dominant MVs are gathered. Meanwhile, bandwidth estimation is statistically characterized by the variation of local standard de-viations; weighted value calculation is determined by estimation of overall standard deviation. Simu-lation results demonstrate their better performance when compared with other MV recovery ap-proaches and low computation cost.

关键词： Error concealment Motion Vector (MV) recovery Mean shift K-means Bandwidth estimation

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