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arXiv 2024年

作者： Ahmad, Muhammad Distefano, Salvatore Khan, Adil Mehmood Mazzara, Manuel Li, Chenyu Li, Hao Aryal, Jagannath Ding, Yao Vivone, Gemine Hong, Danfeng Department of Computer Science National University of Computer and Emerging Sciences Islamabad Pakistan Dipartimento di Matematica e Informatica-MIFT University of Messina Messina98121 Italy School of Computer Science University of Hull HullHU6 7RX United Kingdom Institute of Software Development and Engineering Innopolis University Innopolis420500 Russia Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China Big Geospatial Data Management Technical University of Munich Munich85521 Germany Department of Infrastructure Engineering University of Melbourne ParkvilleVIC Australia Intelligent Control Laboratory PLA Rocket Force University of Engineering Xi’an710025 China Institute of Methodologies for Environmental Analysis National Research Council Tito85050 Italy School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China

Hyperspectral Image Classification (HSC) presents significant challenges owing to the high dimensionality and intricate nature of Hyperspectral (HS) data. While traditional Machine Learning (TML) approaches have demonstrated effectiveness, they often encounter substantial obstacles in real-world applications, including the variability of optimal feature sets, subjectivity in human-driven design, inherent biases, and methodological limitations. Specifically, TML suffers from the curse of dimensionality, difficulties in feature selection and extraction, insufficient consideration of spatial information, limited robustness against noise, scalability issues, and inadequate adaptability to complex data distributions. In recent years, Deep Learning (DL) techniques have emerged as robust solutions to address these challenges. This survey offers a comprehensive overview of current trends and future prospects in HSC, emphasizing advancements from DL models to the increasing adoption of Transformer and Mamba Model architectures. We systematically review key concepts, methodologies, and state-of-the-art approaches in DL for HSC. Furthermore, we investigate the potential of Transformer-based models and the Mamba Model in HSC, detailing their advantages and challenges. Emerging trends in HSC are explored, including in-depth discussions on Explainable AI and Interoperability concepts, alongside Diffusion Models for image denoising, feature extraction, and image fusion. To substantiate the efficacy of various conventional DL models, Transformers, and the Mamba Model, comprehensive experimental results were conducted on three HS datasets, yielding notable accuracies: 99.94% on Pavia University, 99.41% on Indian Pines, and 99.97% on the University of Houston dataset. Additionally, we identify several open challenges and pertinent research questions in the field of HSC. Finally, we outline future research directions and potential applications aimed at enhancing the accuracy and effic

关键词： Convolutional neural networks

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Hyperspectral image denoising and anomaly detection based on low-rank and sparse representations

arXiv

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arXiv 2021年

作者： Zhuang, Lina Gao, Lianru Zhang, Bing Fu, Xiyou Bioucas-Dias, José M. Department of Mathematics Hong Kong BaptistUniversity Hong Kong Hong Kong Key Laboratory of Digital Earth Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China College of Resources and Environment University of Chinese Academy of Sciences Beijing100049 China Shenzhen University Shenzhen518060 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Instituto de Telecomunicações Instituto Superior Técnico Universidade de Lisboa Lisbon1049-001 Portugal

Hyperspectral imaging measures the amount of electromagnetic energy across the instantaneous field of view at a very high resolution in hundreds or thousands of spectral channels. This enables objects to be detected and the identification of materials that have subtle differences between them. However, the increase in spectral resolution often means that there is a decrease in the number of photons received in each channel, which means that the noise linked to the image formation process is greater. This degradation limits the quality of the extracted information and its potential applications. Thus, denoising is a fundamental problem in hyperspectral image (HSI) processing. As images of natural scenes with highly correlated spectral channels, HSIs are characterized by a high level of self-similarity and can be well approximated by low-rank representations. These characteristics underlie the state-of-the-art methods used in HSI denoising. However, where there are rarely occurring pixel types, the denoising performance of these methods is not optimal, and the subsequent detection of these pixels may be compromised. To address these hurdles, in this paper, we introduce RhyDe (Robust hyperspectral Denoising), a powerful HSI denoiser, which implements explicit low-rank representation, promotes self-similarity, and, by using a form of collaborative sparsity, preserves rare pixels. The denoising and detection effectiveness of the proposed robust HSI denoiser is illustrated using semi-real and real data. A MATLAB demo of this work is available at https://***/LinaZhuang for the sake of reproducibility. Copyright © 2021, The Authors. All rights reserved.

关键词： Anomaly detection

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Development of Smart 3D Object Detection using LiDAR Cloud-Data Collection

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Journal of Physics: Conference Series 2021年第1期2128卷

作者： Essam M. Abd Elhamied Sherin M. Youssef Team Leader Development Software Information and Documentation Center Arab Academy for Science & Technology (AAST). Alexandria EGYPT. Department of Computer Engineering Arab Academy for Science & Technology (AAST). Alexandria EGYPT

Smart cities are made up of autonomous vehicle and they communicate and interact with their environment and require high precision computer vision to maintain driver and pedestrian safety. This paper presents a cost-efficient, non-intrusive and easy to use method for collecting data traffic counts using LiDAR technology. The proposed method incorporates a LiDAR sensor, a Convolutional Neural Network (CNN) and a Hybrid SVM into a single traffic counting framework. As the technology is economical and readily accessible, LiDAR is adopted. The distance data obtained are translated into the signals. Due to the difficulty of urban scenes, automatic detection of objects from remotely sensed data within urban areas is difficult. While recent advances in computer vision have shown that CNNs are very suitable for this task, the design and training of CNNs of this kind remained demanding and time consuming, given the challenge of collecting a large and well-annotated dataset and the specificity of every task. Hybrid SVM is a supervised data classification and regression machine learning tool. In the methodology the Hybrid SVM is used in detection and non-detection cases of highly complex distance data points obtained from the sensor. In order to examine the performance of the proposed method, the test is carried out in three different locations in Alexandria, Egypt. The results of tests show that the pro-imposed method achieves acceptable results in vehicle collection, which results in a precision of 85–89%. The exactness of the method proposed is determined by the colour of a vehicle's external surface.

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Deep Learning-based 3D Point Cloud Classification: A Systematic Survey and Outlook

arXiv

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arXiv 2023年

作者： Zhang, Huang Wang, Changshuo Tian, Shengwei Lu, Baoli Zhang, Liping Ning, Xin Bai, Xiao The School of Software Xinjiang University Xinjiang830000 China The Institute of Semiconductors Chinese Academy of Sciences Beijing100083 China Center of Materials Science and Optoelectronics Engineering School of Microelectronics Beijing Key Laboratory of Semiconductor Neural Network Intelligent Sensing and Computing Technology Beijing100083 China University of Chinese Academy of Sciences Beijing100049 China Cognitive Computing Technology Joint Laboratory Wave Group Beijing102208 China Beijing Key Laboratory of Semiconductor Neural Network Intelligent Sensing and Computing Technology Beijing100083 China The School of Computer Science and Engineering Beihang University Beijing100083 China

In recent years, point cloud representation has become one of the research hotspots in the field of computer vision, and has been widely used in many fields, such as autonomous driving, virtual reality, robotics, etc. Although deep learning techniques have achieved great success in processing regular structured 2D grid image data, there are still great challenges in processing irregular, unstructured point cloud data. Point cloud classification is the basis of point cloud analysis, and many deep learning-based methods have been widely used in this task. Therefore, the purpose of this paper is to provide researchers in this field with the latest research progress and future trends. First, we introduce point cloud acquisition, characteristics, and challenges. Second, we review 3D data representations, storage formats, and commonly used datasets for point cloud classification. We then summarize deep learning-based methods for point cloud classification and complement recent research work. Next, we compare and analyze the performance of the main methods. Finally, we discuss some challenges and future directions for point cloud classification. Copyright © 2023, The Authors. All rights reserved.

关键词： Digital storage

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Object Detectors in the Open Environment: Challenges, Solutions, and Outlook

arXiv

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arXiv 2024年

作者： Liang, Siyuan Wang, Wei Chen, Ruoyu Liu, Aishan Wu, Boxi Chang, Ee-Chien Cao, Xiaochun Tao, Dacheng The School of Computing National University of Singapore Singapore117417 Singapore The School of Cyber Science and Technology Shenzhen Campus of Sun Yat-sen University Shenzhen518107 China The Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China The School of Cyber Security University of Chinese Academy of Sciences Beijing100049 China The SCSE Beihang University Beijing100191 China The School of Software Zhejiang University Hangzhou310027 China The School of Computer Science and Engineering Nanyang Technological University #32 Block N4 #02a-014 50 Nanyang Avenue Singapore639798 Singapore

With the emergence of foundation models, deep learning-based object detectors have shown practical usability in closed set scenarios. However, for real-world tasks, object detectors often operate in open environments, where crucial factors (e.g., data distribution, objective) that influence model learning are often changing. The dynamic and intricate nature of the open environment poses novel and formidable challenges to object detectors. Unfortunately, current research on object detectors in open environments lacks a comprehensive analysis of their distinctive characteristics, challenges, and corresponding solutions, which hinders their secure deployment in critical real-world scenarios. This paper aims to bridge this gap by conducting a comprehensive review and analysis of object detectors in open environments. We initially identified limitations of key structural components within the existing detection pipeline and propose the open environment object detector challenge framework that includes four quadrants (i.e., out-of-domain, out-of-category, robust learning, and incremental learning) based on the dimensions of the data / target changes. For each quadrant of challenges in the proposed framework, we present a detailed description and systematic analysis of the overarching goals and core difficulties, systematically review the corresponding solutions, and benchmark their performance over multiple widely adopted datasets. In addition, we engage in a discussion of open problems and potential avenues for future research. This paper aims to provide a fresh, comprehensive, and systematic understanding of the challenges and solutions associated with open-environment object detectors, thus catalyzing the development of more solid applications in real-world scenarios. A project related to this survey can be found at https://***/LiangSiyuan21/OEOD_Survey. Copyright © 2024, The Authors. All rights reserved.

关键词： Object detection

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RFID-Based System on Emergency Medical Care

RFID-Based System on Emergency Medical Care

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International Symposium on Parallel Architectures, Algorithms and Programming (PAAP)

作者： Ruey-Shun Chen Yeh-Cheng Chen Xiaopeng Fan Naixue Xiong Yu-Xi Hu Shi-Jinn Horng S.P. Chen Department of computer engineering Dongguan Polytechnic Dongguan P.R. China The School of Software and Microelectronic Northwestern Polytechnical University Xian P.R. China Department of computer science University of California Davis CA USA Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen P.R. China Institute of Information Management National Chiao Tung University Hsinchu Taiwan National Chiao Tung University Hsinchu TW

ISBN: (纸本)9781538694046;9781538694039

This study is based on RFID technology to detect the locations of medical care personnel and patient who move around indoors; meanwhile, wearable and mobile physiological signal detection suite technique is associated to monitor the physiological status of the patient at any time. Through the positioning message of RFID, the computer system can assign medical care personnel that is nearest to the patient at emergency accident to perform medical care. Furthermore, through the use of wireless mobile device, it can be ensure that medical care personnel can obtain any emergency message of the patient at any time. Through integration of three techniques such as RFID technique, physiological signal detection suite technique and wireless mobile device, a set of emergency medical care system structure is designed in this study. Meanwhile, based on this system structure, a prototype system is developed to verify the feasibility of this system structure. Through the emergency medical care system developed in this study, no matter when and where emergency accident occurs, medical care personnel can reach the patient at the emergency accident site in the shortest time to reduce the possibility of delayed medical care.

关键词： Handheld computers Maintenance engineering RFID tags Medical services Personnel Parallel architectures

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Line drawing technique for building images

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Ruan Jian Xue Bao/Journal of software 2012年第SUPPL.2期23卷 34-41页

作者： Liu, You-Quan Wu, Zong-Sheng Wang, Shan-Dong Xue, Ru Wu, En-Hua School of Information Engineering Chang'an University Xi'an 710064 China Faculty of Science and Technology University of Macau Macao China State Key Laboratory of Computer Science Institute of Software The Chinese Academy of Sciences Beijing 100190 China Tibet University for Nationalities Xianyang 712082 China

This paper presents an automatic line-drawing method designed for building images. The fast line segment detector with a false detection control and detects the outline and the door, or window edge features detected, which are used in the construction of edge tangent flow. Based on the modified ETF, the Difference-of-Gaussians is used to render the final line drawing styles. This scheme gives clear line features for building images without much noise, which is also simple and easy to implement. The test results verify the effectiveness of the presented algorithm. © Copyright 2012, Editorial Department of Journal of software.

关键词： Rendering (computer graphics)

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RobustART: Benchmarking Robustness on Architecture Design and Training Techniques

arXiv

引用

arXiv 2021年

作者： Tang, Shiyu Gong, Ruihao Wang, Yan Liu, Aishan Wang, Jiakai Chen, Xinyun Yu, Fengwei Liu, Xianglong Song, Dawn Yuille, Alan Torr, Philip H.S. Tao, Dacheng The State Key Lab of Software Development Environment Beihang University Beijing100191 China The SenseTime Beijing100191 China The Berkeley Artificial Intelligence Research Lab University of California Berkeley BerkeleyCA94701 United States The Department of Cognitive Science and Computer Science Johns Hopkins University BaltimoreMD21218 United States The Department of Engineering Science University of Oxford OxfordOX1 3PJ United Kingdom The JD Explore Academy Beijing101111 China

Deep neural networks (DNNs) are vulnerable to adversarial noises, which motivates the benchmark of model robustness. Existing benchmarks mainly focus on evaluating defenses, but there are no comprehensive studies of how architecture design and training techniques affect robustness. Comprehensively benchmarking their relationships is beneficial for better understanding and developing robust DNNs. Thus, we propose RobustART, the first comprehensive Robustness investigation benchmark on ImageNet regarding ARchitecture design (49 human-designed off-the-shelf architectures and 1200+ networks from neural architecture search) and Training techniques (10+ techniques, e.g., data augmentation) towards diverse noises (adversarial, natural, and system noises). Extensive experiments substantiated several insights for the first time, e.g., (1) adversarial training is effective for the robustness against all noises types for Transformers and MLP-Mixers;(2) given comparable model sizes and aligned training settings, CNNs > Transformers > MLP-Mixers on robustness against natural and system noises;Transformers > MLP-Mixers > CNNs on adversarial robustness;(3) for some light-weight architectures, increasing model sizes or using extra data cannot improve robustness. Our benchmark http://***/presents: (1) an open-source platform for comprehensive robustness evaluation;(2) a variety of pre-trained models to facilitate robustness evaluation;and (3) a new view to better understand the mechanism towards designing robust DNNs. We will continuously develop to this ecosystem for the community. Copyright © 2021, The Authors. All rights reserved.

关键词： computer vision

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Verifiable Privacy Preservation Scheme for Outsourcing Medi-cal Image to Cloud Through ROI Based Crypto-Watermarking

Verifiable Privacy Preservation Scheme for Outsourcing Medi-...

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IEEE/ACM Int'l Conference on & Int'l Conference on Cyber, Physical and Social Computing (CPSCom) Green Computing and Communications (GreenCom)

作者： Chuan Zhou Yi Zhou XingHan An Yan Liu Min Wang XiangZhi Liu Tianjin University Tianjin China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China College of Environmental Science and Engineering Beijing Forestry University Beijing China Inspur Software Co. Ltd. Jinan China The Party School of cpc Jinan Municipal Committee Jinan China

A novel verifiable privacy preservation scheme for outsourcing medical image to cloud through ROI based crypto-watermarking is proposed in the paper. In the proposed scheme, data owner firstly carries out substitution of S-box for the region of interest (ROI) of medical image, and then separates the image into 4 most significant bits (MSBs) plane and 4 least significant bits (LSBs) plane images. Secondly, the hash value of ROI is embedded into the two separated bit plane images using reversible watermarking algorithm. Lastly, some selected hash values are transformed into the initial parameters of chaotic maps, and the two sharing secrets, which are produced through chaos based encryption algorithm, are finally outsourced to two different cloud servers. For authorized users, they can get shares from different cloud servers, and then can losslessly recover the original medical image through a series of decryption operations and extraction of watermarking. Furthermore, the users can verify whether the original image is completely reconstructed or not, they even can locate the tampered parts inside ROI if anyone of the sharing secrets is damaged. Some experiments analyses and comparisons are given to show the security and effectiveness of proposed scheme.

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Ai outperformed every dermatologist: Improved dermoscopic melanoma diagnosis through customizing batch logic and loss function in an optimized deep cnn architecture

arXiv

引用

arXiv 2020年

作者： PHAM, CONG TRI LUONG, MAI CHI VAN HOANG, DUNG DOUCET, ANTOINE School of Computer Science and Engineering Thuyloi University 175 Tay Son Dong Da Hanoi Viet Nam ICTLab University of Science and Technology of Hanoi Vietnam Academy of Science and Technology 18 Hoang Quoc Viet Cau Giay Hanoi Viet Nam FPT Software 17 Duy Tan Street Cau Giay district Ha Noi Viet Nam Institute of Information Technology Vietnam Academy of Science and Technology 18 Hoang Quoc Viet Cau Giay Hanoi Viet Nam QuangBinh University Dong Hoi Quang Binh Viet Nam Laboratory L3i University La Rochelle France

Melanoma, one of most dangerous types of skin cancer, re-sults in a very high mortality rate. Early detection and resection are two key points for a successful cure. Recent research has used artificial in-telligence to classify melanoma and nevus and to compare the assess-ment of these algorithms to that of dermatologists. However, an imbal-ance of sensitivity and specificity measures affected the performance of existing models. This study proposes a method using deep convolution-al neural networks aiming to detect melanoma as a binary classification problem. It involves 3 key features, namely customized batch logic, customized loss function and reformed fully connected layers. The training dataset is kept up to date including 17,302 images of melanoma and nevus;this is the largest dataset by far. The model performance is compared to that of 157 dermatologists from 12 university hospitals in Germany based on MClass-D dataset. The model outperformed all 157 dermatologists and achieved state-of-the-art performance with AUC at 94.4% with sensitivity of 85.0% and specificity of 95.0% using a pre-diction threshold of 0.5 on the MClass-D dataset of 100 dermoscopic images. Moreover, a threshold of 0.40858 showed the most balanced measure compared to other researches, and is promisingly application to medical diagnosis, with sensitivity of 90.0% and specificity of 93.8%. Copyright © 2020, The Authors. All rights reserved.

关键词： Dermatology

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