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ASME 43rd International Conference on Ocean, Offshore and Arctic Engineering (OMAE)

作者： Yu, Qifeng Han, Yudong Shanghai Maritime Univ Coll Transport & Commun Shanghai Peoples R China

ISBN: (纸本)9780791887806

The harsh marine atmospheric conditions, including high temperatures, humidity, and salt spray, prevalent in the coastal areas of Hainan, pose a significant challenge to the durability of ground facilities and equipment, often resulting in corrosion and functional degradation. Hence, accurate monitoring of corrosion is paramount for maintaining coastal engineering structures. This study leveraged data from the Chinese National Center for Materials Corrosion and Protection Science to extract corrosion morphology features using image analysis techniques. Subsequently, a corrosion identification model was developed using machine learning algorithms. The model's accuracy was validated through various evaluation metrics. The research outcomes have practical applications in the maintenance and management of coastal engineering structures by providing automatic corrosion morphology recognition. This enables maintenance personnel to promptly undertake repair measures, thereby reducing maintenance costs and enhancing structural sustainability.

关键词： Corrosion morphology machine learning techniques image processing YOLO v5 automatic recognition

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A Review on Generative Adversarial Networks: Algorithms, Theory, and applications

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2023年第4期35卷 3313-3332页

作者： Gui, Jie Sun, Zhenan Wen, Yonggang Tao, Dacheng Ye, Jieping Southeast Univ Sch Cyber Sci & Engn Nanjing 211100 Jiangsu Peoples R China Purple Mt Labs Nanjing 210000 Peoples R China Univ Michigan Dept Computat Med & Bioinformat Ann Arbor MI USA Chinese Acad Sci Ctr Res Intelligent Percept & Comp Beijing 100190 Peoples R China Nanyang Technol Univ Sch Comp Sci & Engn Singapore 639798 Singapore JD Explore Acad Beijing Peoples R China Univ Sydney Sch Comp Sci Camperdown Australia Beike Beijing 100085 Peoples R China Univ Michigan Ann Arbor MI 48109 USA

Generative adversarial networks (GANs) have recently become a hot research topic;however, they have been studied since 2014, and a large number of algorithms have been proposed. Nevertheless, few comprehensive studies explain the connections among different GAN variants and how they have evolved. In this paper, we attempt to provide a review of the various GAN methods from the perspectives of algorithms, theory, and applications. First, the motivations, mathematical representations, and structures of most GAN algorithms are introduced in detail, and we compare their commonalities and differences. Second, theoretical issues related to GANs are investigated. Finally, typical applications of GANs in image processing and computer vision, natural language processing, music, speech and audio, the medical field, and data science are discussed.

关键词： Generators Generative adversarial networks Data models Linear programming Natural language processing machine learning algorithms Inference algorithms Deep learning generative adversarial networks algorithm theory applications

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Bio-inspired smart vision sensor: toward a reconfigurable hardware modeling of the hierarchical processing in the brain

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JOURNAL OF REAL-TIME image processing 2021年第1期18卷 157-174页

作者： Bhowmik, Pankaj Pantho, Md Jubaer Hossain Bobda, Christophe Univ Florida Dept Elect & Comp Engn Gainesville FL 32611 USA

Biological vision systems inspire processing methods in computer vision applications. This paper employs the insights of vision systems in hardware and presents a pixel-parallel, reconfigurable, and layer-based hierarchical architecture for smart image sensors. The architecture aims to bring computation close to the sensor to achieve high acceleration for different machine vision applications while consuming low power. We logically divide the image into multiple regions and perform pixel-level and region-level processing after removing spatiotemporal redundancy. Those processors use bio-inspired algorithms to activate the regions with region of interest of a scene. The hierarchical processing breaks the traditional sequential image processing and introduces parallelism for machine vision applications. Also, we make the hardware design reconfigurable even after fabrication to make the hardware reusable for different applications. Simulation results show that the area overhead and power penalty for adding reconfigurable features stay in an acceptable range. We emphasize to maximize the operating speed and obtain 800 MHz. Besides, the design saves 84.01% and 96.91% dynamic power at the first and second stages of the hierarchy by removing redundant information. Furthermore, the sequential deployment of high-level reasoning only on the selected regions of the image becomes computationally inexpensive to execute a complex task in real time.

关键词： Biological vision Pixel-level processing Reconfigurability Predictive coding Attention module Smart image sensor FPGA ASIC

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Sub-pixel dimensional and vision measurement method of eccentricity or annular parts

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APPLIED OPTICS 2022年第6期61卷 1531-1538页

作者： Fang, Yuntao Wang, Xiaodong Xin, Yunpeng Luo, Yi Dalian Univ Technol Key Lab Micro Nano Technol & Syst Liaoning Prov Dalian Peoples R China Dalian Univ Technol Minist Educ Key Lab Precis & Nontradit Machining Dalian Peoples R China

Eccentricity measurement of annular parts with millimeter scale and micrometer precision requirements is widely used in mechanical engineering applications. To realize accurate eccentricity measurement for large-scaled annular parts, a vision-based and sub-pixel dimensional measurement method is proposed. First, to facilitate the eccentricity measurement, an improved auto focus algorithm is introduced to provide better focused images of the measured parts. Then the traditional Canny operator is modified in gradient direction calculation and a double threshold process to locate the pixel edge more accurately. Next, a model-based sub-pixel edge detection method is studied to extract the sub-pixel edge coordinates. Finally, the eccentricity is calculated according to these sub-pixel edge coordinates. To guarantee measurement accuracy, the pixel equivalent and manual installation error of three degree of freedom (DOF) stages are calibrated, and the verification experiments indicate that the measurement error of the proposed method is better than 1.0 mu m. (C) 2022 Optica Publishing Group

关键词： Autofocus Digital image processing Edge detection image processing Laser sensors machine vision

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A machine Learning Approach to image Registration Verification

A Machine Learning Approach to Image Registration Verificati...

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Multimodal image Exploitation and Learning 2023

作者： DelMarco, Stephen Webb, Helen Stolle, Frank Bortolami, Simone Tucker, Frank Hillyer, Travis BAE Systems 600 District Avenue BurlingtonMA01803 United States BAE Systems 600 District Avenue BurlingtonMA01803 United States CCDC Soldier Center STTC 12423 Research Pkwy OrlandoFL32826 United States

ISBN: (数字)9781510661677

ISBN: (纸本)9781510661660

image registration is an important pre-processing step for many image exploitation algorithms such as geo-location, object recognition, vision-aided navigation, and image fusion. The utility and effectiveness of downstream exploitation algorithms depends on reliable image registration. image registration failure can corrupt processing and performance of downstream algorithms by mis-associating image features. For example, feature mis-association leads to generation of incorrect target geo-coordinates in aerial surveillance applications, or erroneous vision-based measurements in vision aided navigation. Accurate and dependable registration failure detection mitigates deleterious effects of erroneous registration. However, in autonomous operation modes, with no human in-the-loop, and in the absence of registration solution groundtruth knowledge, verifying registration solutions is problematic. In this paper we present a machine learning based image registration verification system that operates autonomously, without ground-truth. We train a machine learning algorithm to identify correct registration solutions, even for difficult multi-modal image registration in which sensor phenomenology differences produce different feature manifestation. The verification approach includes techniques for mitigation of falsealarms that may arise due to feature ambiguity. We present examples of feature ambiguity for correlation-related registration techniques. We describe Radon transform processing, covariance estimation, and fusion techniques for feature ambiguity detection. We present numerical verification performance results from a small pilot study designed to investigate the feasibility of using machine learning for reliable registration verification. © 2023 SPIE.

关键词： image registration

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An Improved Reversible Data Hiding Scheme Based on Referred Frequencies for a VQ Compressed Index 2

An Improved Reversible Data Hiding Scheme Based on Referred ...

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2nd International Conference on machine vision, image processing and Imaging Technology, MVIPIT 2024

作者： Hwang, Min-Shiang Fatima, Kanza Wang, Yu-Lun Chan, Chi-Shiang Wu, Chia-Chun Department of Computer Science & Information Engineering Fintech and Blockchain Research Center Asia University Taichung Taiwan Department of Computer Science & Information Engineering Asia University Taichung Taiwan Department of Management Information Systems National Chung Hsing University Taichung Taiwan Department of M-Commerce and Multimedia Applications Asia University Taichung Taiwan Department of Computer Science and Information Engineering National Quemoy University Kinmen Taiwan

ISBN: (纸本)9798331543037

Nowadays, we usually compress images before uploading them to social media. However, images on social media can easily be copied, so embedding secret messages in compressed images has become increasingly popular. There are many compression methods, such as Huffman, VQ, ZIP, AMBTC, RAR, JPEG, etc. In this article, we propose an improved data hiding in VQ compression method to achieve better capacity and high quality. Experimental results show that our data-hiding approach is practical. ©2024 IEEE.

关键词： compressed image data hiding LSB matching reversible data hiding steganography vector quantization

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Frame-Adaptive Multi-Object Tracking for Discontinuous image Sequences 2

Frame-Adaptive Multi-Object Tracking for Discontinuous Image...

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2nd International Conference on Algorithm, image processing and machine vision, AIPMV 2024

作者： Li, Xiaolei Xiao, Xingjie Yang, Siyuan Sha, Moquan Sha, Zongyao Tu, Jianguang School of Remote Sensing and Information Engineering Wuhan University Wuhan China Wuhan Luojia Zhongheng Remote Sensing Data Technology Co Wuhan China China Unicom Smart City Research Institute Beijing China

ISBN: (纸本)9798350390254

In the field of multi-object tracking, this study introduces an innovative framework designed to address the challenges posed by frame loss in image sequences, particularly within the contexts of video surveillance and traffic management. Despite the significant achievements of deep learning-based tracking methods in processing high frame rate videos, frame loss remains a prevalent issue in real-world deployments due to network and hardware constraints, which can result in erroneous target associations and interrupted trajectory tracking. Our research begins by detecting frame loss through a timestamp- based analysis, this is followed by the deployment of an adaptive algorithm that fuses spatial and appearance metrics to compensate for the positional discrepancies and visual inconsistencies caused by missing frames, thus improving the precision of data association. We have curated a dataset that accurately reflects real-world environments, and conducted validation on subsampled versions of the MOT17 and MOT20 datasets, in addition to our original dataset. Empirical results indicate that our multi-object tracking approach demonstrates significant adaptability and robustness in handling discontinuous image sequences, reducing the number of ID switches by 54.1 % on our original dataset compared to state-of-the-art methods. The proposed framework has the potential to significantly enhance the performance of multi-object tracking systems in real-world scenarios where frame loss is a common occurrence, thereby improving the reliability and accuracy of video surveillance and traffic management applications. © 2024 IEEE.

关键词： Adaptive algorithms

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Efficient dual-wavelength metasurface for second-order differential edge detection in the ultraviolet

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APPLIED OPTICS 2023年第32期62卷 8552-8557页

作者： Chen, Hao Gui, Lili Guo, Jinyang Xu, Kun Beijing Univ Posts & Telecommun State Key Lab Informat Photon & Opt Commun Beijing 100876 Peoples R China

Metasurfaces for edge detection through spatial analog calculations have attracted much attention due to advantages such as a flexible design and small footprint. Up until now, most studies have focused on single-wavelength operation in the near-infrared or visible regions, while little work has been done in the ultraviolet band. It is of significance to explore metasurfaces for edge detection in the ultraviolet band for their great potential in highresolution imaging and lithography. Here, we propose a dual-wavelength HfO2 metasurface for edge detection working at 273 nm and 293 nm, with 25% and 72% efficiency, respectively, controlled by the linear polarization of the incident light. The efficient dual-wavelength second-order differential calculation in the ultraviolet band of the metasurface has been confirmed by 1D signal and 2D image processing. It may find applications in the fields of computer vision and bioimaging. (c) 2023 Optica Publishing Group

关键词： Edge detection Imaging systems Lithography machine vision Optical transfer functions Polarization control

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Successfully implemented artificial intelligence and machine learning applications in cardiology: State-of-the-art review

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TRENDS IN CARDIOVASCULAR MEDICINE 2023年第5期33卷 265-271页

作者： Van den Eynde, Jef Lachmann, Mark Laugwitz, Karl-Ludwig Manlhiot, Cedric Kutty, Shelby Johns Hopkins Univ Blalock Taussig Thomas Pediat & Congenital Heart C Johns Hopkins Sch Med Dept Pediat Baltimore MD USA Katholieke Univ Leuven Dept Cardiovasc Sci Leuven Belgium Tech Univ Munich Dept Med 1 Klinikum Rechts Isar Munich Germany DZHK German Ctr Cardiovasc Res Partner Site Munich Heart Alliance Munich Germany Johns Hopkins Univ Hosp 600 NWolfe St1389 Blalock Baltimore MD 21287 USA

The omnipresence and deep impact of artificial intelligence (AI) in today's society are undeniable. While the technology has already established itself as a powerful tool in several industries, more recently it has also started to change the practice of medicine. The aim of this review is to provide healthcare providers working in the field of cardiovascular medicine with an overview of AI and machine learning (ML) algorithms that have passed the initial tests and made it into contemporary clinical practice. The following domains where AI/ML could revolutionize cardiology are covered: (i) signal processing, (ii) image processing, (iii) clinical risk stratification, (iv) natural language processing, and (v) fundamental clinical discoveries.

关键词： Artificial intelligence Cardiac imaging techniques Deep learning machine learning Precision medicine

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machine Learning Algorithms for Signal and image processing 1

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2022年

作者： Deepika Ghai Suman Lata Tripathi Sobhit Saxena Manash Chanda Mamoun Alazab

ISBN: (数字)9781119861850

ISBN: (纸本)9781119861829

Enables readers to understand the fundamental concepts of machine and deep learning techniques with interactive, real-life applications within signal and image processing machine Learning Algorithms for Signal and image processing aids the reader in designing and developing real-world applications using advances in machine learning to aid and enhance speech

关键词： speech signal processing image processing computer vision biomedical signal processing adaptive filtering text processing pre-processing feature extraction source separation data decompositions machine learning tasks Speech recognition image reconstruction object classification and detection Healthcare monitoring biomedical systems green energy sign language recognition fake news detection in social media structural damage prediction epileptic seizure detection

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