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Conference on Multimodal image Exploitation and Learning

作者： Halford, Gavin Depoian, Arthur C., ii Bailey, Colleen P. Univ North Texas Dept Elect Engn Denton TX 76207 USA

ISBN: (纸本)9781510673854;9781510673847

Low-light image enhancement plays a crucial role for applications in security, photography, medical imaging, and scientific research. Traditional enhancement methods, including multi-spectral hardware and contrast adjustments via computer vision, often fall short due to current hardware limitations or the sparse data available in low-light conditions. This paper introduces an innovative approach that significantly improves the brightness and overall quality of low-light images, focusing on enhanced feature extraction. Our method efficiently and accurately compensates for missing data in real-time, making it highly suitable for scenarios that demand immediate processing. This is particularly beneficial for surveillance applications, where the clarity of images is essential for swift decision-making.

关键词： LOL Dataset Low-Light Enhancement Efficient AI

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Hardware and Software Integration of machine Learning vision System Based on NVIDIA Jetson Nano 1

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Future of Information and Communication Conference, FICC 2024

作者： Manolescu, Denis Reid, David Secco, Emanuele Lindo Robotics Lab School of Mathematics Computer Science and Engineering Liverpool Hope University Liverpool United Kingdom

ISBN: (数字)9783031540530

ISBN: (纸本)9783031540523

This study investigates the capabilities and flexibility of edge devices for real-time data processing near the source. A configurable Nvidia Jetson Nano system is used to deploy nine pre-trained computer vision models, demonstrating proficiency in local data processing, analysis and providing real-time feedback. Additionally, the system offers deployment control via a customized Graphical User Interface (GUI) and proves very low-latency inference re-stream to other local devices using the G-Streamer framework. The machine Learning models which cover a wide range of applications, including image classification, object recognition or detection, depth estimation and semantic segmentation, show potential for IoT and industrial applications. Further, the fusion of these capabilities with AI and machine learning algorithms unveils a promising perspective for substantial industrial redevelopment. This research underscores the strategic significance of edge devices in modern computational frameworks and their potential role in future technological advancements. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Computer vision

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Advancements in machine visions for fruit sorting and grading: A bibliometric analysis, systematic review, and future research directions

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JOURNAL OF AGRICULTURE AND FOOD RESEARCH 2024年 16卷

作者： Olorunfemi, Benjamin Oluwamuyiwa Nwulu, Nnamdi I. Adebo, Oluwafemi Ayodeji Kavadias, Kosmas A. Univ Johannesburg Ctr Cyber Phys Food Energy & Water Syst ZA-2006 Johannesburg South Africa Univ Johannesburg Fac Sci Ctr Innovat Food Res Dept Biotechnol & Food Technol POB 17011Doornfontein Campus ZA-2028 Johannesburg South Africa Univ West Attica Dept Mech Engn Lab Soft Energy Applicat & Environm Protect 250 Thivon & P Ralli StrCampus Ancient Olive Grov Athens Greece

This research conducted a bibliometric analysis of scholarly literature on fruit sorting and grading using machine vision, identifying primary themes, sources, most -cited publications, and countries. The literature and bibliometric analysis were thoroughly evaluated to consolidate knowledge, identify research trends, and propose specific research opportunities within the context of machine vision for fruit sorting and grading. Research articles from 2011 to 2023, indexed in the main collections of the Dimensions, Web -of -science, and Scopus databases, were examined. Findings were presented quantitatively, using tables and graphs to emphasize the key performance factors for article writing and citation. Upon applying inclusion and exclusion criteria, 129 out of 1812 discovered articles were included for examination, while 1683 studies were excluded due to noncompliance with the requirements and duplicates. Thirty-four (34) case study publications on machine vision applications for fruit sorting and grading were comprehensively examined to identify the adopted methodologies and future research opportunities. Covered methodologies include fruit varieties, data volumes, data collection, classification methods, and accuracy metrics. The study's findings indicate a significant increase in deep learning applications for fruit recognition in the recent five years (2019-2023), with excellent results achieved either by utilizing new models or with pre -trained networks for transfer learning. The research also identifies gaps and future directions for machine vision in fruit sorting and grading, such as enhancing system robustness, scalability, and adaptability, integrating multiple sensors and technological methods, and developing evaluation and comparison standards and criteria. The paper concludes that machine vision holds promise as a potent tool for fruit quality assessment, but further research and development are needed to address existing challenges and meet the grow

关键词： Fruit sorting Fruit grading Computer vision image processing Deep learning

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Metasurface-enabled 3D imaging via local bright spot gray scale matching using the structured light dot array

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OPTICS LETTERS 2024年第21期49卷 6325-6328页

作者： Zhang, Zhengren Sun, Qian Qu, Anjun Yang, Mengran Li, Zile Chongqing Jiaotong Univ Sch Mat Sci & Engn Chongqing 400074 Peoples R China Chongqing Jiaotong Univ Sch Informat Sci & Engn Chongqing 400074 Peoples R China Wuhan Univ Elect Informat Sch Wuhan 430072 Peoples R China Wuhan Univ Sch Microelect Wuhan 430072 Peoples R China

Three-dimensional (3D) imaging is widely utilized in various applications, such as light detection, autonomous vehicles, and machine vision. However, conventional 3D imaging systems often rely on bulky optical components. Metasurfaces, as next-generation optical devices, possess flexible wavefront modulation capabilities and excellent combination with computer vision algorithms. Here, we propose a large field-of-view (FOV) structured light dot array projection device based on a metasurface, covering a 2 pi-FOV, for projecting coded point clouds in Fourier space. We explore a local bright spot gray scale matching algorithm for depth extraction, enabling 3D imaging. This algorithm simplifies the data processing flow and optimizes depth extraction and feature matching processes through a customized region gray scale comparison. As a result, it effectively reduces computational complexity and enhances tolerance to image quality fluctuations. The proposed approach provides new possibilities for developing compact and high-performance planar 3D optical imaging devices, which will drive the advancement of fields such as computer vision and artificial intelligence. (c) 2024 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.

关键词： image enhancement Imaging systems Optical components Optical imaging Structured light Three dimensional imaging

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Multiple auxiliary classifiers GAN for controllable image generation: Application to license plate recognition

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IET INTELLIGENT TRANSPORT SYSTEMS 2023年第1期17卷 243-254页

作者： Shvai, Nadiya Abul Hasnat Nakib, Amir Cydope Ai VINCI Autoroutes Paris France Univ Paris Est Creteil Lab LISSI Vitry Sur Seine France

One of the main challenges in developing machine learning (ML) applications is the lack of labeled and balanced datasets. In the literature, different techniques tackle this problem via augmentation, rendering, and over-sampling. Still, these methods produce datasets that appear less natural, exhibit poor balance, and have less variation. One potential solution is to leverage the Generative Adversarial Network (GAN) which achieves remarkable results in the generation of high-fidelity natural images. However, expanding the ability of GANs' to control generated image attributes with supervisory information remains a challenge. This research aims to propose an efficient method to generate high-fidelity natural images with total control of its main attributes. Therefore, this paper proposes a novel Multiple Auxiliary Classifiers GAN (MAC-GAN) framework based on Auxiliary Classifier GAN (AC-GAN), multi-conditioning, Wasserstein distance, gradient penalty, and dynamic loss. It is therefore presented as an efficient solution for highly controllable image synthesis red that allows to enrich and re-balance datasets beyond data augmentation. Furthermore, the effectiveness of MAC-GAN images on a target ML application called Automatic License Plate Recognition (ALPR) under limited resource constraints is probed. The improvement achieved is over 5% accuracy, which is mainly due to the ability of the MAC-GAN to create a balanced dataset with controllable synthesis and produce multiple (different) images with the same attributes, thus increasing the variation of the dataset in a more elaborate way than data augmentation techniques.

关键词： traffic engineering computing total control auxiliary classifier GAN image recognition data augmentation techniques machine learning applications learning (artificial intelligence) multiple auxiliary classifiers GAN framework MAC-GAN images controllable synthesis pattern classification automatic license plate recognition Neural nets generative adversarial network AC-GAN Computer vision and image processing techniques generated image attributes neural nets Traffic engineering computing Data handling techniques image recognition controllable image generation highly controllable image synthesis red balanced dataset ALPR high-fidelity natural images data augmentation labeled balanced datasets target ML application

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machine vision-based Automatic Liquid Dispensing Metering Technology and System for Microfluids 11

Machine Vision-based Automatic Liquid Dispensing Metering Te...

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Optical Metrology and Inspection for Industrial applications XI 2024

作者： Wang, Hao Cao, Feifan Xiao, Wei Qian, Xiang Wang, Xiaohao Li, Xinghui Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen518055 China Yunjing Technology Co. Ltd. Guilin541004 China Tsinghua-Berkeley Shenzhen Institute Tsinghua University Shenzhen518055 China

ISBN: (纸本)9781510682108

In modern scientific research and industrial applications, the rapid, automated, and accurate measurement of micro-liquid volumes added to reaction or detection containers is a critical need. Traditional methods for measuring micro-liquid volumes often suffer from insufficient accuracy, low stability, and are prone to interference from bubbles between micro-liquids and residual droplets in the transmission pipelines. To address these issues, this paper proposes an automated micro-liquid metering method and system based on machine vision. The system comprises optical imaging units, drive control units, image processing units, metering algorithm units, and calibration units. By optimizing the optical imaging setup, the brightness and contrast of the liquid in the metering field are enhanced, ensuring the accuracy of the volume measurement. Additionally, image processing algorithms are employed to segment the liquid section, and its length in the pixel coordinate system is extracted as a representation of the volume, effectively eliminating the interference from bubbles in the image. Finally, calibration-based measurement methods and direct measurement methods based on homography matrix scale transformation of marker points achieve metering accuracies of 98.2% and 98.3%, respectively. Compared to traditional industrial micro-liquid metering methods, this approach effectively overcomes the impact of bubbles on measurement accuracy while offering greater stability and reliability. © 2024 SPIE.

关键词： image segmentation

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image Edge Enhancement Detection Method of Human-Computer Interaction Interface Based on machine vision Technology

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MOBILE NETWORKS & applications 2022年第2期27卷 775-783页

作者： Jin, Yi Wei, Wei Suzhou Vocat Univ Jiangsu Prov Support Software Engn R&D Ctr Modern Dept Comp Engn Suzhou 215104 Peoples R China Xian Univ Technol Sch Comp Sci & Engn Xian 710048 Peoples R China Shaanxi Key Lab Network Comp & Secur Technol Xian Peoples R China

Aiming at the problems of low detection accuracy and long detection time of existing image edge detection technologies, an image edge detection method of human-computer interaction interface based on machine vision technology is proposed. Based on machine vision technology, the image weight is calculated by iterative repeated weighted least square method, the image is Gaussian filtered by improved Canny algorithm, and the optimal threshold is calculated by iterative method to judge the effective edge. Through comparative experiments, it is proved that the maximum detection accuracy of the man-machine interface image edge enhancement detection method based on machine vision technology proposed in this paper is 100%, the detection time is always kept below 0.2S, and the fastest detection time is 0.1 s, which has wide applicability.

关键词： machine vision technology image edge enhancement detection Canny algorithm Least square method Gaussian filtering

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Switchable Two-Dimensional AND and Exclusive OR Operation Based on Dual-Wavelength Metasurfaces

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ACS NANO 2024年第5期18卷 4424-4431页

作者： Peng, Chang Huang, Tian Chen, Chen Liu, Hongchao Liang, Xiao Li, Zile Yu, Shaohua Zheng, Guoxing Wuhan Univ Elect Informat Sch Wuhan 430072 Peoples R China Wuhan Univ Sch Microelect Wuhan 430072 Peoples R China Peng Cheng Lab Shenzhen 518055 Peoples R China Wuhan Inst Quantum Technol Wuhan 430206 Peoples R China Chinese Acad Sci Suzhou Inst Nanotech & Nanobion Suzhou 215123 Peoples R China Univ Macau Inst Appl Phys & Mat Engn Macau 999078 Macao Peoples R China

Logic operation serves as the foundation and core element of computing networks;it will bring huge vitality to advanced information processing with its adaptation in the optical domain. As fundamental logic operations, AND and exclusive OR (XOR) operations serve a multitude of purposes, such as their ability to cooperate in enabling image processing and interpretation. Here, we propose and experimentally demonstrate a wavelength multiplexed AND and XOR function based on metasurfaces. By combining two cosine gratings with distinct frequencies and an initial phase difference of pi/2, we extract the similarities and differences between two input images simultaneously by illuminating them with 445 and 633 nm wavelengths. Additionally, we explore its potential in information encryption, where overall security is enhanced by distributing distinct parts of initial information and encoded keys to different receivers. This design possesses the benefits of convenient mode switching and high-quality imaging, facilitating advanced applications in pattern recognition, machine vision, medical diagnosis, etc.

关键词： metasurfaces two-dimensional logic operation wavelength multiplexing optical image processing information hiding

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Graphene/MoS_(2-x)O_(x)/graphene photomemristor with tunable non-volatile responsivities for neuromorphic vision processing

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Light(Science & applications) 2023年第2期12卷 284-293页

作者： Xiao Fu Tangxin Li Bin Cai Jinshui Miao Gennady NPanin Xinyu Ma jinjin Wang Xiaoyong Jiang Qing Li Yi Dong Chunhui Hao Juyi Sun Hangyu Xu Qixiao Zhao Mengjia Xia Bo Song Fansheng Chen Xiaoshuang Chen Wei Lu Weida Hu School of Physics and Optoelectronic Engineering Hangzhou Institute for Advanced StudyUniversity of Chinese Academy of SciencesHangzhou 310024China State Key Laboratory of Infrared Physics Shanghai Institute of Technical PhysicsChinese Academy of SciencesShanghai 200083China University of Chinese Academy of Sciences 100049 BeijingChina Institute of Intelligent Machines HFiPSChinese Academy of SciencesHefei 230031China Jianghuai Frontier Technology Coordination and Innovation Center Hefei 230088China Institute of Microelectronics Technology and High-Purity Materials Russian Academy of SciencesChernogolovkaMoscow district142432Russia

Conventional artificial intelligence(Al)machine vision technology,based on the von Neumann architecture,uses separate sensing,computing,and storage units to process huge amounts of vision data generated in sensory *** frequent movement of redundant data between sensors,processors and memory,however,results in high-power consumption and latency.A more efficient approach is to offload some of the memory and computational tasks to sensor elements that can perceive and process the optical signal ***,we proposed a non-volatile photomemristor,in which the reconfigurable responsivity can be modulated by the charge and/or photon flux through it and further stored in the *** non-volatile photomemristor has a simple two-terminal architecture,in which photoexcited carriers and oxygen-related ions are coupled,leading to a displaced and pinched hysteresis in the current-voltage *** the frst time,non-volatile photomemristors implement computationally complete logic with photoresponse-stateful operations,for which the same photomemristor serves as both a logic gate and memory,using photoresponse as a physical state variable instead of light,voltage and *** polarity reversal of photomemristors shows great potential for in-memory sensing and computing with feature extraction and image recognition for neuromorphic vision.

关键词： tunable operations hysteresis

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From Templates to Transformers: A survey of Multimodal image Captioning Decoders

From Templates to Transformers: A survey of Multimodal Image...

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2023 International Conference on Computer, Electronics and Electrical Engineering and their applications, IC2E3 2023

作者： Sharma, Himanshu Padha, Devanand Central University of Jammu Department of Computer Science and It Rahya-Suchani Jammu India

ISBN: (纸本)9798350338003

Automated text-to-image (image synthesis) and image-to-text (image captioning) generation are two of the most challenging and cutting-edge fields of study in Computer vision (CV) in conjunction with Natural Language processing (NLP). The image-to-text synthesis, also known as image Captioning (IC), has numerous applications in visual assistance, machine vision, remote security, healthcare, and remote sensing, among others. Since their origin, the IC frameworks have been constructed utilizing a two-subcomponent pipeline consisting of the visual feature extraction and natural language modeling subcomponents. The recent surge of interest in the applications of sequential deep learning models in machine translation has resulted in the development of efficient language modeling architectures. Deep sequential modeling architectures, such as Recurrent Neural Networks (RNN), Long Short Term Memory (LSTM), and Gated Recurrent Units (GRU), tackle the intricacies of the multi-modular space far better than existing machine learning-based translation approaches. The tremendous growth of deep language modeling architectures in IC necessitates a comprehensive review of its literature. In this survey, we conduct an exhaustive and analytical analysis of the language modeling architectures used in IC frameworks for caption generation, along with their training corpus datasets. We also identify a list of open research issues and potential research areas for future work. © 2023 IEEE.

关键词： Natural language processing systems

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