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6th IEEE International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2023

作者： Qin, Yusong Huang, Pu Zhou, Yao Huang, Zengxi Xihua University School of Computer Science and Software Engineering Chengdu China Xihua University School of Computer and Software Engineering and the Sichuan Xihua Jiaotong Forensics Center Chengdu China

ISBN: (纸本)9798350325485

Person re-identification(Re-ID) is a crucial task in computer vision, which aims to match pedestrian images captured in non-overlapping camera views. It has significant implications for public safety applications. However, current methods primarily rely on global features and thus are vulnerable to background clutters and occlusion. To address these issues, this paper proposes a part-based Re-ID method using the Swin Transformer. In this method, we first utilize human pose estimation method to detect the body keypoints of a pedestrian. We then extract local windows and patches centered at these keypoints, so as to minimize the involvement of background and occlusion information in the down-stream re-identification. These body parts in pose guided windows are fed to the Swin Transformer network. The hierarchical structure of Swin Transformer also enables the proposed method more robust to multi-scale problems. Our experimental results on both holistic and occluded datasets, i.e., Market-1501, DukeMTMC-reID, and Occluded-Duke, demonstrate that the proposed method is on par with the state-of-The-Art methods. © 2023 IEEE.

关键词： Clutter (information theory)

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Two-Stage Lesion Detection Approach Based on Dimension-Decomposition and 3D Context

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Tsinghua science and Technology 2022年第1期27卷 103-113页

作者： Jiacheng Jiao Haiwei Pan Chunling Chen Tao Jin Yang Dong Jingyi Chen the College of Computer Science and Technology Harbin Engineering UniversityHarbin 150001China the School of Software Tsinghua UniversityBeijng 100084China

Lesion detection in Computed Tomography(CT) images is a challenging task in the field of computer-aided *** important issue is to locate the area of lesion *** a branch of Convolutional Neural Networks(CNNs),3D Context-Enhanced(3DCE) frameworks are designed to detect lesions on CT *** False Positives(FPs) detected in 3DCE frameworks are usually caused by inaccurate region proposals,which slow down the inference *** solve the above problems,a new method is proposed,a dimension-decomposition region proposal network is integrated into 3DCE framework to improve the location accuracy in lesion *** the restriction of "anchors" on ratios and scales,anchors are decomposed to independent "anchor strings".Anchor segments are dynamically combined in accordance with probability,and anchor strings with different lengths dynamically compose bounding *** show that the accurate region proposals generated by our model promote the sensitivity of FPs and spend less inference time compared with the current methods.

关键词： lesion detection Computed Tomography(CT) dimension-decomposition 3D context computer-aided diagnosis

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Multi-Objective Optimization of Multi-Product Parallel Disassembly Line Balancing Problem Considering Multi-Skilled Workers Using a Discrete Chemical Reaction Optimization Algorithm

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computers, Materials & Continua 2024年第9期80卷 4475-4496页

作者： Xiwang Guo Liangbo Zhou Zhiwei Zhang Liang Qi Jiacun Wang Shujin Qin Jinrui Cao College of Information and Control Engineering Liaoning Petrochemical UniversityFushun113000China Department of Computer Science and Technology Shandong University of Science and TechnologyQingdao266590China Department of Computer Science and Software Engineering Monmouth UniversityWest Long BranchNJ 07764USA Research Center of the Economic and Social Development of Henan East Provincial Joint Shangqiu Normal UniversityShangqiu476000China Department of Computer Science New Jersey City UniversityJersey CityNJ 07305USA

This work investigates a multi-product parallel disassembly line balancing problem considering multi-skilled workers.A mathematical model for the parallel disassembly line is established to achieve maximized disassembly profit and minimized workstation cycle *** on a product’s AND/OR graph,matrices for task-skill,worker-skill,precedence relationships,and disassembly correlations are developed.A multi-objective discrete chemical reaction optimization algorithm is *** enhance solution diversity,improvements are made to four reactions:decomposition,synthesis,intermolecular ineffective collision,and wall invalid collision reaction,completing the evolution of molecular *** established model and improved algorithm are applied to ball pen,flashlight,washing machine,and radio combinations,*** a Collaborative Resource Allocation(CRA)strategy based on a Decomposition-Based Multi-Objective Evolutionary Algorithm,the experimental results are compared with four classical algorithms:MOEA/D,MOEAD-CRA,Non-dominated Sorting Genetic Algorithm Ⅱ(NSGA-Ⅱ),and Non-dominated Sorting Genetic Algorithm Ⅲ(NSGA-Ⅲ).This validates the feasibility and superiority of the proposed algorithm in parallel disassembly production lines.

关键词： Parallel disassembly line balancing problem multi-product multiskilled workers discrete chemical reaction optimization algorithm

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Generative Adversarial Networks (GANs) for Image Synthesis and Augmentation 4

Generative Adversarial Networks (GANs) for Image Synthesis a...

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4th IEEE International Conference on Electronic Technology, Communication and Information, ICETCI 2024

作者： Wen, Yuxin Mei, Zhenhao Qi, Chuyue Xinjiang University Department of Software Engineering Urumqi China Hefei University of Technology School of Computer Science and Information Engineering Hefei China

ISBN: (纸本)9798350361643

In the realm of medical imaging, a scarcity of reliable, sizable datasets for training supervised deep learning models persists. One solution involves leveraging Generative Adversarial Networks (GANs) to fabricate synthetic deepfake images, bridging this gap and enriching available data. Deepfake technology seamlessly transfers crucial features from source to target images or videos, mimicking the source with remarkable fidelity. Over the past decade, propelled by cutting-edge deep learning techniques and vast datasets, medical image processing has made monumental strides. Supervised deep learning models now achieve superhuman performance across diverse medical imaging applications. Synthetic deepfake images, produced through techniques like GANs, serve various purposes, including dataset balancing, modal translation, and expansion. To efficiently augment training data for deep learning, a hierarchical GAN (HGAN) framework is proposed, focusing on generating high-quality knee images. HGAN integrates spectral normalization in discrimination and pixel normalization in generation, enhancing training stability. Evaluation metrics such as AM Score and Mode Score are employed to benchmark HGAN against alternatives like PGGAN, reflecting its efficacy in advancing medical imaging research and application. © 2024 IEEE.

关键词： Medical imaging

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Dynamic Multimedia Encryption Using a Parallel File System Based on Multi-Core Processors 4

Dynamic Multimedia Encryption Using a Parallel File System B...

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4th IEEE International Conference on Data engineering and Communication Systems, ICDECS 2024

作者： Simi Margarat, G. Malathi, S. Pasumpon Pandian, A. Anakath, A.S. Kannadasan, R. New Prince Shri Bhavani College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Grt Institute of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Tiruttani India Care College of Engineering Department of Computer Science and Engineering Trichy India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India School of Computer Science and Engineering Department of Software Systems Tamil Nadu Vellore India

ISBN: (纸本)9798350393354

A new parallel file system and multi-core processor-based dynamic multimedia encryption method is presented in this study. Multimedia encryption efficiency and security were the main goals, addressing massive data sets and high-speed processing. We created a dynamic encryption algorithm for multimedia content, supported by a strong parallel file system and tuned for multi-core processing. Multiple tests showed significant improvements in encryption and decryption times, retaining short durations for real-time applications. After encryption, the system maintained multimedia content quality and accuracy with data integrity rates nearing 100%. Balanced CPU utilization showed efficient resource use and processor load management. The system's excellent throughput efficiency showed its capacity to process enormous amounts of data. These results indicate that parallel file systems and multi-core CPUs may solve multimedia encryption problems. Adaptable and scalable, the system supports many multimedia formats and sizes. This paper proposes a multimedia encryption system that improves performance data security and integrity, paving the way for safe multimedia processing advances. © 2024 IEEE.

关键词： Cryptography

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A Metamodel for a DSL to Design Decision Support Systems in Smart Tourism

A Metamodel for a DSL to Design Decision Support Systems in ...

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2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Ouaddi, Charaf Benaddi, Lamya Souha, Adnane Naimi, Lahbib Bouziane, El Mahi Jakimi, Abdeslam Moulay Ismail University Software Engineering and Information Systems Engineering Team Faculty of Science and Technology Errachidia Department of Computer Sciences Morocco

ISBN: (纸本)9798350369106

The development of Decision Support Systems (DSS) for several companies operating in sectors such as tourism, healthcare, or others, presents significant challenges due to the nature of their multi-component architecture. A typical DSS consists of a data component, a model component incorporating machine learning and deep learning techniques, and a user interface component, each one requires diverse technologies. This complexity increases development time and effort, hindering the efficient design of DSS tailored to specific domains like tourism, where real-time decision-making is essential. One promising approach to overcome these challenges is the use of Domain-Specific Languages (DSLs). DSLs allow developers to model and generate code more efficiently by providing high-level abstractions suited to the domain. By leveraging DSLs, it is possible to simplify the design process and automate the generation of code or parts of the DSS, thereby reducing the overall complexity and time required for development. This paper proposes a metamodel for designing DSS, specifically for the Smart Tourism sector. The metamodel serves as a foundation for the creation of DSL. The goal is to provide a solution to facilitate the development of DSS for the demands of tourism. The paper outlines the key concepts, approach, and results, presenting the first element of the proposed DSL which is the metamodel capturing the essential components and relationships for DSS in Smart Tourism. © 2024 IEEE.

关键词： Digital subscriber lines

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Multi-Scale Fusion and Decomposition Network for Single Image Deraining

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IEEE Transactions on Image Processing 2024年 33卷 191-204页

作者： Wang, Qiong Jiang, Kui Wang, Zheng Ren, Wenqi Zhang, Jianhui Lin, Chia-Wen Wuhan University National Engineering Research Center for Multimedia Software School of Computer Science Wuhan430072 China Sun Yat-sen University School of Cyber Science and Technology Guangzhou510275 China Hangzhou Dianzi University College of Computer Science and Technology Hangzhou310018 China National Tsing Hua University Department of Electrical Engineering The Institute of Communications Engineering Hsinchu30013 Taiwan

Convolutional neural networks (CNNs) and self-attention (SA) have demonstrated remarkable success in low-level vision tasks, such as image super-resolution, deraining, and dehazing. The former excels in acquiring local connections with translation equivariance, while the latter is better at capturing long-range dependencies. However, both CNNs and Transformers suffer from individual limitations, such as limited receptive field and weak diversity representation of CNNs during low efficiency and weak local relation learning of SA. To this end, we propose a multi-scale fusion and decomposition network (MFDNet) for rain perturbation removal, which unifies the merits of these two architectures while maintaining both effectiveness and efficiency. To achieve the decomposition and association of rain and rain-free features, we introduce an asymmetrical scheme designed as a dual-path mutual representation network that enables iterative refinement. Additionally, we incorporate high-efficiency convolutions throughout the network and use resolution rescaling to balance computational complexity with performance. Comprehensive evaluations show that the proposed approach outperforms most of the latest SOTA deraining methods and is versatile and robust in various image restoration tasks, including underwater image enhancement, image dehazing, and low-light image enhancement. The source codes and pretrained models are available at https://***/qwangg/MFDNet. © 1992-2012 IEEE.

关键词： Image reconstruction

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Bridging Fourier and Spatial-Spectral Domains for Hyperspectral Image Denoising 24

Bridging Fourier and Spatial-Spectral Domains for Hyperspect...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Xiao, Jiahua Liu, Yang Zhang, Shizhou Wei, Xing School of Software Engineering Xi'an Jiaotong University Xi'an China School of Computer Science and Engineering Northwest Polytechnical University Xi'an China

ISBN: (纸本)9798400706868

Remarkable progresses have been made in hyperspectral image (HSI) denoising. However, the majority of existing methods are predominantly confined to the spatial-spectral domain, overlooking the untapped potential inherent in the Fourier domain. This paper presents a novel approach to address HSI denoising by bridging the information from the Fourier and spatial-spectral domains. Our method highlights key insights into the Fourier properties within spatial and spectral domains through the Fourier transform. Specifically, we note that the amplitude inherently embody noise and photon reflection characteristics, while the phase holds structural information. These insights unveil new perspectives on the physical properties of HSIs, motivating us to leverage complementary information exchange between Fourier and spatial-spectral domains. To this end, we introduce the Fourier-prior Integration Denoising Network (FIDNet), a potent yet straightforward approach that utilizes Fourier insights to synergistically interact with spatial-spectral domains for superior HSI denoising. In FIDNet, we independently extract spatial and Fourier features through dual branches and merge these representations to enhance spectral evolution modeling through the inherent structure consistency constraints and continuing reflection variation revealed in Fourier prior. Our proposed method demonstrates robust generalization across synthetic and real-world benchmark datasets, achieves comparable results with state-of-the-art methods in both quantitative quality and visual results. The code is available at https://***/MIV-XJTU/FIDNet. © 2024 ACM.

关键词： Image denoising

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Pre-trained SAM as data augmentation for image segmentation

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CAAI Transactions on Intelligence Technology 2025年第1期10卷 268-282页

作者： Junjun Wu Yunbo Rao Shaoning Zeng Bob Zhang Yangtze Delta Region Institute(Huzhou) University of Electronic Science and Technology of ChinaHuzhouChina School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengduChina Pattern Analysis and Machine Intelligence Research Group Department of Computer and Information ScienceUniversity of MacaoMacaoChina

Data augmentation plays an important role in training deep neural model by expanding the size and diversity of the ***,data augmentation mainly involved some simple transformations of ***,in order to increase the diversity and complexity of data,more advanced methods appeared and evolved to sophisticated generative ***,these methods required a mass of computation of training or *** this paper,a novel training-free method that utilises the Pre-Trained Segment Anything Model(SAM)model as a data augmentation tool(PTSAM-DA)is proposed to generate the augmented annotations for *** the need for training,it obtains prompt boxes from the original annotations and then feeds the boxes to the pre-trained SAM to generate diverse and improved *** this way,annotations are augmented more ingenious than simple manipulations without incurring huge computation for training a data augmentation *** comparative experiments on three datasets are conducted,including an in-house dataset,ADE20K and *** this in-house dataset,namely Agricultural Plot Segmentation Dataset,maximum improvements of 3.77%and 8.92%are gained in two mainstream metrics,mIoU and mAcc,***,large vision models like SAM are proven to be promising not only in image segmentation but also in data augmentation.

关键词： data augmentation image segmentation large model segment anything model

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Early Diagnosis of Lung Tumors for Extending Patients’ Life Using Deep Neural Networks

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computers, Materials & Continua 2023年第7期76卷 993-1007页

作者： A.Manju R.Kaladevi Shanmugasundaram Hariharan Shih-Yu Chen Vinay Kukreja Pradip Kumar Sharma Fayez Alqahtani Amr Tolba Jin Wang Department of Computer Science and Engineering SRM Institute of Science and TechnologyRamapuramChennaiIndia Department of Computer Science and Engineering Saveetha Engineering CollegeChennaiIndia Department of Computer Science and Engineering Vardhaman College of EngineeringHyderabadIndia Department of Computer Science and Information Engineering National Yunlin University of Science and TechnologyYunlin 64002Taiwan Intelligence Recognition Industry Service Research Center National Yunlin University of Science and TechnologyYunlin 64002Taiwan Chitkara University Institute of Engineering and Technology Chitkara UniversityPunjabIndia Department of Computing Science University of AberdeenAberdeenUK Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh 12372Saudi Arabia Computer Science Department Community CollegeKing Saud UniversityRiyadh 11437Saudi Arabia School of Computer&Communication Engineering Changsha University of Science&TechnologyChangshaChina

The medical community has more concern on lung cancer *** experts’physical segmentation of lung cancers is time-consuming and needs to be *** research study’s objective is to diagnose lung tumors at an early stage to extend the life of humans using deep learning ***-Aided Diagnostic(CAD)system aids in the diagnosis and shortens the time necessary to detect the tumor *** application of Deep Neural Networks(DNN)has also been exhibited as an excellent and effective method in classification and segmentation *** research aims to separate lung cancers from images of Magnetic Resonance Imaging(MRI)with threshold *** Honey hook process categorizes lung cancer based on characteristics retrieved using several *** this principle,the work presents a solution for image compression utilizing a Deep Wave Auto-Encoder(DWAE).The combination of the two approaches significantly reduces the overall size of the feature set required for any future classification process performed using *** proposed DWAE-DNN image classifier is applied to a lung imaging dataset with Radial Basis Function(RBF)*** study reported promising results with an accuracy of 97.34%,whereas using the Decision Tree(DT)classifier has an accuracy of 94.24%.The proposed approach(DWAE-DNN)is found to classify the images with an accuracy of 98.67%,either as malignant or normal *** contrast to the accuracy requirements,the work also uses the benchmark standards like specificity,sensitivity,and precision to evaluate the efficiency of the *** is found from an investigation that the DT classifier provides the maximum performance in the DWAE-DNN depending on the network’s performance on image testing,as shown by the data acquired by the categorizers themselves.

关键词： Lung tumor deep wave auto encoder decision tree classifier deep neural networks extraction techniques

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