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image Fusion Algorithm Based on Spatial Frequency-Motivated Pulse Coupled Neural Networks in Nonsubsampled Contourlet Transform Domain

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Acta Automatica Sinica 2008年第12期34卷 1508-1514页

作者： Xiao-Bo QU Jing-Wen YAN Hong-Zhi XIAO Zi-Qian ZHU Department of Communication Engineering Xiamen University Xiamen 361005 P.R.China Key Laboratory of Digital Signal and Image Processing of Guangdong Province Shantou University Shantou 515063 P. R. China Research Institute of Chinese Radar Electronic Equipment Wuxi 214063 P.R.China

Nonsubsampled contourlet transform (NSCT) provides flexible multiresolution, anisotropy, and directional expansion for images. Compared with the original contourlet transform, it is shift-invariant and can overcome the pseudo-Gibbs phenomena around singularities. Pulse coupled neural networks (PCNN) is a visual cortex-inspired neural network and characterized by the global coupling and pulse synchronization of neurons. It has been proven suitable for image processing and successfully employed in image fusion. In this paper, NSCT is associated with PCNN and used in image fusion to make full use of the characteristics of them. Spatial frequency in NSCT domain is input to motivate PCNN and coefficients in NSCT domain with large firing times are selected as coefficients of the fused image. Experimental results demonstrate that the proposed algorithm outperforms typical wavelet-based, contourlet-based, PCNN-based, and contourlet-PCNN-based fusion algorithms in terms of objective criteria and visual appearance.

关键词： Contourlet pulse coupled neural networks (PCNN) wavelet image fusion multiscale transform

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On Wireless Local Area Networks Network Traffic

On Wireless Local Area Networks Network Traffic

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Chinese Control Conference (CCC)

作者： Rong Hong Liang Yiming Wu Ronghui Xu Cheng Yu Fe College of Electrical and Information Engineering Hunan University Changsha China College of Computer & Communication Hunan University Changsha China Provincial Key Laboratory of Image Processing & Imagecom Communication Nanjing University of Posts and Telecommunications Nanjing China

Through a series of real tests, this paper analyses the characteristic of IEEE 802.11 traffic, explains the results of the tests. More importantly, the results indicate that the characteristic of IEEE 802.11 traffic exhibits obviously self-similarity. As the first step to set up a 802.11 traffic math-model based on self-similarity theory.

关键词： Wireless LAN Telecommunication traffic Traffic control Educational institutions Testing communication system traffic control image processing Telecommunication computing

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Novel Regularization Method for Reduced Biquaternion Neural Network

SSRN

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SSRN 2023年

作者： Gai, Shan Huang, Xiang School of Information Science and Engineering Yanshan University Hebei Qinhuangdao066004 China Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Hangkong University Jiangxi Nanchang330063 China

A reduced biquaternion neural network (RQNN) is a new type of neural network framework that has achieved significant success in machine learning. However, as the reduced biquaternion algebra system contains infinite zero divisors, the RQNN is easily trapped in a local minimum and overfitted. We propose a new regularization scheme for the RQNN to address these issues. First, we propose a new operation in the reduced biquaternion domain—the reduced biquaternion complex modulus (RQCM). Second, we analyse the algebraic properties and obtain the geometric meaning of the RQCM. Finally, we design an improved weight decay method using the RQCM. Unlike conventional weight decay methods with matrix norm constraints, the proposed method can better project the reduced biquaternion in terms of the scale and phase. Colour image classification and denoising tasks are used to demonstrate the superior performance of the RQCM over state-of-the-art weight decay methods. The source code can be found at https://***/tasteimage/Reg4RQNN . © 2023, The Authors. All rights reserved.

关键词： Complex networks

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Theoretical mechanism and research progress of thermally activated delayed fluorescence and room temperature phosphorescence molecules

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分子科学学报 2024年第6期40卷 471-487页

作者： ZHAO Xin SONG Yuzhi LIN Lili WANG Chuankui FAN Jianzhong Shandong Province Key Laboratory of Medical Physics and Image Processing Technology Institute of Materials and Clean EnergySchool of Physics and ElectronicsShandong Normal UniversityJinan 250014China State Key Laboratory of Luminescent Materials and Devices South China University of TechnologyGuangzhou 511442China

Thermally activated delayed fluorescence(TADF)and room temperature phosphorescence(RTP)molecules hold promising application prospects in the field of organic light emitting diodes(OLEDs),primarily attributed to their significant advantages in enhancing device stability and lumines-cence ***,TADF and RTP molecules can achieve nearly 100%exciton utilization without necessitating costly and limited precious metal ***,the primary challenges confronting TADF and RTP molecules at present encompass limitations in emission color,low luminescence efficien-cy,severe efficiency roll-off and so *** these points,this paper presents a comprehensive overview of the latest research progress in TADF and RTP *** delve into the mechanisms by which TADF molecules achieve efficient fluorescence emission through unique molecular structural designs,fre-quently involving sophisticated intramolecular charge transfer processes and precise energy level ***,we provide an in-depth analysis of the unique luminescence properties and photo-physical mechanisms of RTP ***,the article focuses on the design strategies for TADF and RTP molecules,encompassing the manipulation of molecular structures,electronic structures and the enhancement of charge transfer *** examining these strategies,we aim to provide a com-prehensive perspective on the research of TADF and RTP *** hope that through this review,it could offer some guidance for future research and inspire the exploration of more innovative TADF and RTP molecules.

关键词： thermally activated delayed fluorescence room temperature phosphorescence excited state property TVCF method QM/MM method

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Energy management of solid oxide fuel cell/lithium battery hybrid system

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CSEE Journal of Power and Energy Systems 2022年 1-14页

作者： Li, Dong Zhong, Xiao Bo Xu, Yuan Wu Wei, Chong Yang Zhao, Song Jiang, Jianhua Deng, Zhonghua Fu, Xiaowei Li, Xi School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control of Education Ministry Huazhong University of Science and Technology Hubei Wuhan430074 China College of Computer Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Hubei Wuhan430081 China

The research of fuel cell and lithium battery hybrid system has attracted more and more researchers because of its advantages of low emission. However, the lower efficiency of energy management has been a critical factor that obstruct the commercialization of hybrid system. In this work, based on finite element ideas, a solid oxide full cell node model is developed to accurately estimate the operating status of the fuel cell. As for lithium battery, previous energy management studies generally only focused on state of charge, however, state of health is also a key parameter for lithium battery. In this paper, considering the strong coupling between variables we use particle filter algorithm to jointly estimate the state of charge and state of health. To reasonably distribute and manage the energy in the hybrid system, a T-S fuzzy controller for the hybrid system is designed by controlling the state variables in the SOFC model and the state of lithium battery. Finally, our algorithm is verified by DSPACE simulation platform. The result shows that the hybrid system with the energy management strategy not only meets the real-time sharply changing demand but also ensures the hybrid system working efficiently and safely. © 2022 China Electric Power Research Institute. All rights reserved.

关键词： Hybrid systems

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The design and implementation of a Chinese financial invoice recognition system

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Journal of Quantitative Linguistics 2002年第1期9卷 19-33页

作者： Ming, Delie Liu, Jian Tian, Jinwen State Key Laboratory for Image Processing and Intelligent Control Institute for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan Hubei Province 430074 China

This paper designs and implements a financial invoice recognition system based on the features of the Chinese financial invoice. By using the linear whole block moving method in each vertical segment, a new fast algorithm is put forth to detect and rectify the slant image. To distinguish the different form types (the foundation necessary for locating the form fields, filtering the form lines, etc.), several representative form features are discussed and an invoice-type features library is built by using a semi-automatic machine study method. On the basis of the recognized invoice type, real invoice form is re-oriented against the corresponding blank form according to the invoice type feature, solving the problem of adhesion of characters and form lines, as well as the problem of character segmentation and recognition. Based on the Chinese financial invoice image feature, a mutual rectification mechanism founded on the recognition results of Chinese financial characters and Arabic numerals is put forward to raise the recognition rate. Finally, the experimental results and conclusions are presented. © Swets & Zeitlinger.

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Online Pedestrian Tracking Using Ensemble Color Feature

Online Pedestrian Tracking Using Ensemble Color Feature

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International Conference on Computer and Information Technology (CIT)

作者： Bo Zhang Yi Xu Xiaokang Yang Department of Electronic Engineering Shanghai Jiao Tong University Cooperative Medianet Innovation Center Shanghai Key Laboratory of Digital Media Processing and Transmissions Institute of Image Communication and Network Engineering Shanghai China

In this paper, we propose a novel online visual tracking algorithm using ensemble color feature. Visual tracking has long been one of the most important topics in computer vision. In recent years, significant progress has been achieved by the state-of-the-art tracking algorithms. However, some challenging problems still remain unsolved. One general problem is that it is difficult for a tracking algorithm to achieve both high accuracy and efficiency. More specifically, most tracking algorithms base on the hypothesis that the tracking object is a rigid body. Thus, some visual features are disabled when the hypothesis is invalid. Meanwhile, the existing trackers tend to fail when the target object changes poses, because such changes lead to the dramatic mismatch between the momentaneous appearance model and the learned one. The last problem is especially serious when the tracking targets are pedestrians, which suffer irregular pose and shape changes while they walk or run. The proposed algorithm applies ensemble color feature model. It takes pedestrians' physiological structure into consideration, and takes advantage of color histogram information under several color spaces, including the RGB, normRGB, HSV, and Lab. Since histogram is a statistic-based feature, and the garments of pedestrians provide abundant color information, our appearance model using ensemble color feature is more robust to pose and shape changes. Experiments on Caltech Pedestrian Database with evaluation of the state-of-the-art algorithms show better performance of the proposed algorithm.

关键词： Color image color analysis Target tracking Visualization Algorithm design and analysis Histograms Shape

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A new automatic mass detection method for breast cancer with false positive reduction

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Neurocomputing 2015年 152卷 388-402页

作者： Xiaoming Liu Zhigang Zeng School of Automation Huazhong University of Science and Technology Wuhan 430074 China Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan 430074 China College of Computer Science and Technology Wuhan University of Science and Technology Wuhan 430081 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan 430081 China

Mass localization is a crucial problem in computer-aided detection (CAD) system for the diagnosis of suspicious regions in mammograms. In this paper, a new automatic mass detection method for breast cancer in mammographic images is proposed. Firstly, suspicious regions are located with an adaptive region growing method, named multiple concentric layers (MCL) approach. Prior knowledge is utilized by tuning parameters with training data set during the MCL step. Then, the initial regions are further refined with narrow band based active contour (NBAC), which can improve the segmentation accuracy of masses. Texture features and geometry features are extracted from the regions of interest (ROI) containing the segmented suspicious regions and the boundaries of the segmentation. The texture features are computed from gray level co-occurrence matrix (GLCM) and completed local binary pattern (CLBP). Finally, the ROIs are classified by means of support vector machine (SVM), with supervision provided by the radiologist׳s diagnosis. To deal with the imbalance problem regarding the number of non-masses and masses, supersampling and downsampling are incorporated. The method was evaluated on a dataset with 429 craniocaudal (CC) view images, containing 504 masses. Among them, 219 images containing 260 masses are used to optimize the parameters during MCL step, and are used to train SVM. The remaining 210 images (with 244 masses) are used to test the performance. Masses are detected with 82.4% sensitivity with 5.3 false positives per image (FPsI) with MCL, and after active contour refinement, feature analysis and classification, it obtained 1.48 FPsI at the sensitivity 78.2%. Testing on 164 normal mammographic images showed 5.18 FPsI with MCL and 1.51 FPsI after classification. Experiments on mediolateral oblique (MLO) images have also been performed, the proposed method achieved a sensitivity 75.6% at 1.38 FPsI. The method is also analyzed with free response operating characteristi

关键词： Mass detection Breast cancer Level set Local binary pattern Support vector machine

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A Survey: Target Tracking Algorithm Based on Sparse Representation

A Survey: Target Tracking Algorithm Based on Sparse Represen...

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International Symposium on Computational Intelligence and Design, ISCID

作者： Dan Lu Linsheng Li Qingsen Yan Institute of Digital Media & Communication Taiyuan University of Science and Technology Taiyuan China Shaanxi Key Laboratory of Speech & Image Information Processing School of Computer Science Northwestern Polytechnical University Xi'an China

ISBN: (纸本)9781479970063

In this paper, we introduce the development of object tracking. In particular, we introduce several kinds of target tracking algorithm based on sparse coding, including a robust visual tracking method by casting tracking as a sparse approximation problem in a particle filter framework, kernel sparse tracking with compressive sensing, and real-time compressive tracking. Show the concept of sparse representation and compressed sensing, analyze the meaning of the sparse representation in the target tracking, and compare the algorithm.

关键词： Target tracking Kernel Robustness Visualization image coding Pattern recognition Compressed sensing

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Visual comfort assessment of stereoscopic images with multiple salient objects

Visual comfort assessment of stereoscopic images with multip...

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

作者： Xiaonan Zhang Jun Zhou Jianyu Chen Xiaoqiang Guo Ya Zhang Xiao Gu Shanghai Key Laboratory of Digital Media Processing and Transmissions Shanghai China Institute of Image Communication and Network Engineering Shanghai Jiao Tong University Shanghai China Academy of Broadcasting Science Beijing China

This paper focuses on discomfort assessment issue for S3D images with multiple salient objects, and proposes four kinds of visual features of S3D images that have potential correlations with visual discomfort, including disparity distribution features, disparity jump features, object distribution features as well as object width features. A stereoscopic image database is designed focusing on multiple salient object scenarios and prediction model is built using support vector regression based on the proposed features. Prediction performance evaluation result shows that the proper combinations of the proposed features can achieve effective visual discomfort prediction for these types of pictures, and also shows that the convergence adjustments induced by the disparity discrepancies among the salient objects do cause visual discomfort in these scenarios.

关键词： Visualization Stereo image processing Feature extraction Three-dimensional displays image databases Convergence

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