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2023 International Conference on Big Data Mining and Information processing, BDMIP 2023

作者： Liu, Guoyan Zhao, Jincai Zeng, Yanan Wu, Haiyun Li, Zhi Wei, Yong College of Technology Tianjin Agricultural University Tianjin China

ISBN: (纸本)9798400709166

The detection and morphology characterization of these biological samples are the basis of life research. Optical microscopic imaging has great advantages in the characterization and detection of biological samples because of its characteristics of low sample requirements, good environmental adaptability, convenient, fast and non-destructive detection. However, due to the influence of optical diffraction limit, the resolution of optical microscopic imaging method is comparative low and thus limits the applications. Due to the polarization characteristics of the scattering field from scatterer are closely related to the microstructure of the scatterer, the measurement of the polarization characteristics of the scattering field can improve the detection and characterization ability of biological samples. Meanwhile, to achieve a higher resolution and to obtain a larger range of particle near-field scattering spectral distribution, the method in the paper takes the image acquisition part of the traditional machine vision system as the main body and a polarization modulation module is added to build an image acquisition device. The non-intuitive images obtained in the experiment are compared with the traditional direct imaging, and the results prove that the non-intuitive polarization parameter imaging can obtain the particle near-field scattering spectral distribution in a larger range than the traditional direct imaging. It is proved that non-intuitive light wave vectors are more sensitive to near-field scattering and have higher resolution. © 2023 ACM.

关键词： Computer vision

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Advances in Computer vision and Deep Learning and Its applications

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ELECTRONICS 2025年第8期14卷 1551-1551页

作者： Wang, Aili Wu, Haibin Iwahori, Yuji Harbin Univ Sci & Technol Heilongjiang Prov Key Lab Laser Spect Technol & Ap Harbin 150080 Peoples R China Chubu Univ Dept Comp Sci 1200 Matsumoto cho Kasugai 4878501 Japan

(1) Computer vision: The field of computer vision is making significant strides in dynamic reasoning capability through test-time scaling (TTS) [1] technology. TTS optimizes the robustness and interpretability of models in complex tasks by flexibly allocating computational resources. Multimodal base models, such as CLIP (contrastive language-image pre-training) [2] and Florence, facilitate the deep fusion of vision and language through cross-modal alignment techniques. These advancements have significantly improved the accuracy of visual question answering (VQA) and cross-modal retrieval. Generative AI technologies, such as Stable Diffusion, have also broken through the limitations of 2D image generation, enabling the transition to semantics-driven 3D scene models, like neural radiance fields (NeRF) [3]. This shift supports the generation of spatial models with physically interactive attributes from a single sheet of input, providing a new paradigm for virtual reality and industrial design. In addition, the introduction of the spatial intelligence [4] concept allows computer vision systems to simulate physical interactions in 3D space, driving the development of embodied intelligence and robot *** articles included in this Special Issue cover advancements in ten research directions: computer vision, feature extraction and image selection, pattern recognition for image processing techniques, image processing in intelligent transportation, neural networks, machine learning and deep learning, biomedical image processing and recognition, image processing for intelligent surveillance, deep learning for image processing, robotics and unmanned systems, and AI-based image processing, understanding, recognition, compression, and reconstruction. I have categorized the 33 articles included in this Special Issue based on these research directions, with the classification system not only demonstrating the vertical extension of the technological depth but also embodyin

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An ultra-fast and energy-efficient CNTFET-based image corner detection hardware for real-time image processing applications

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AEU-INTERNATIONAL JOURNAL OF ELECTRONICS AND COMMUNICATIONS 2024年 175卷

作者： Behbahani, Fereshteh Behrad, Alireza Moaiyeri, Mohammad Hossein Shahed Univ Elect Engn Dept Tehran Iran Shahid Beheshti Univ Fac Elect Engn Tehran Iran

In image processing and machine vision, corner detection is pivotal in diverse applications, including computer vision, 3D reconstruction, face detection, object tracking, and video technologies. Despite the wide usage, the real-time and energy -efficient hardware implementation of corner detection algorithms remains a critical challenge because of the computational resource limitations. On the other hand, owing to the complicated nature of corner detection algorithms, their hardware implementation has been limited to the graphics processing unit (GPU) and field programmable gate arrays (FPGA) platforms. In this regard, this work aims to propose a novel and ultra -efficient carbon nanotube field-effect transistor (CNTFET)-based hardware for image corner detection. Thanks to the proposed corner detection algorithm, the designed hardware has been realized using 2742 transistors with competitive accuracy. The proposed corner detection hardware indicated a remarkable salt -and -pepper noise immunity without using any noise reduction circuit. Our comprehensive simulations demonstrate 78%, 87%, and 94.5% total average improvements in delay, power, and energy compared to the other related corner detection hardware. Moreover, the proposed CNTFET-based corner detection hardware shows a 43 ps propagation delay, demonstrating its real-time operation. The proposed corner detection algorithm at the system level shows suitable accuracy metrics such as Recall, Precision, and error of detection (EoD) compared to the other well-known corner detectors. Our method has established a new pathway for real-time circuit -level hardware design for image processing and machine vision applications.

关键词： Fuzzy logic Programmable MFG FinFET Slope calibration image enhancement

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A Survey on Optimal Transport for machine Learning: Theory and applications

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IEEE ACCESS 2025年 13卷 26506-26526页

作者： Pereira, Luiz Manella Amini, M. Hadi Florida Int Univ Knight Fdn Sch Comp & Informat Sci Miami FL 33199 USA Florida Int Univ Secur Optimizat & Learning InterDependent Networks Miami FL 33199 USA

Optimal Transport (OT) theory has seen increasing attention from the computer science community due to its potency and relevance in modeling and machine learning (ML). OT provides powerful tools for comparing probability distributions and producing optimal mappings that minimize cost functions. Consequently, OT has been widely implemented in computer vision tasks such as image retrieval, image interpolation, and semantic correspondence, as well as in broader applications spanning domain adaptation, natural language processing, and variational inference. In this survey, we aim to convey the emerging prominence and widespread applications of OT methods across various ML areas and outline future research directions. We first provide a history of OT. We then introduce a mathematical formulation and the prerequisites to understand OT, including Kantorovich duality, entropic regularization, KL Divergence, and Wasserstein barycenters. Given the computational complexity of OT, we discuss entropy-regularized version of computing optimal mappings that facilitate practical applications of OT across diverse ML domains. Further, we review prior studies on OT applications in ML. To this end, we cover the following: computer vision, graph learning, neural architecture search, document representation, domain adaptation, model fusion, medicine, natural language processing, and reinforcement learning. Finally, we outline future research directions and key challenges that could drive the broader integration of OT in ML.

关键词： Measurement Costs Surveys Natural language processing Interpolation Earth Couplings Computer vision US Department of Homeland Security Reinforcement learning machine learning optimal transport Wasserstein distance computational optimal transport

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A survey and identification of generative adversarial network technology-based architectural variants and applications in computer vision

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INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT 2024年第9期15卷 4594-4615页

作者： Zala, Kirtirajsinh Thumar, Deep Thakkar, Hiren Kumar Maheshwari, Urva Acharya, Biswaranjan Marwadi Univ Dept Informat Technol Rajkot 360006 Gujarat India Marwadi Educ Fdn Fac Engn Rajkot 360006 Gujarat India Pandit Deendayal Energy Univ Sch Technol Dept Comp Sci Engn Gandhinagar 382007 Gujarat India Marwadi Univ Dept Comp Engn AI & BDA Rajkot 360006 Gujarat India

The surging popularity of generative adversarial networks (GANs) has ignited a wave of innovation in the realm of computer vision, a highly explored subfield of deep learning. GANs are revolutionizing the area of machine learning because they use a game-based training technique. This is in contrast to traditional approaches to machine learning, which center on feature learning and picture production. Several subfields of computer vision have seen tremendous progress thanks to the integration of numerous processing approaches, including image processing, dynamic processing, text, audio, and video processing, as well as generalized generative adversarial networks (GANs). Nevertheless, despite the fact that GANs have made great progress, they still offer promise that has not been fully realized and space for additional development. GANs have a wide range of applications within computer vision, including data augmentation, displacement recording, dynamic modeling, and image processing. This article digs into recent advances made by GAN researchers working in the realm of AI-based security and defense and discusses their accomplishments. In particular, we investigate how well image optimization, image processing, and image stabilization are incorporated into GAN-driven picture training. We want to achieve our goal of providing a complete overview of the present status of GAN research by carefully evaluating research articles that have been subjected to peer review.

关键词： Generative adversarial networks Computer vision Loss-variants machine learning Intelligent computing Bio informatics

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Semantic segmentation of agricultural images:A survey

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Information processing in Agriculture 2024年第2期11卷 172-186页

作者： Zifei Luo Wenzhu Yang Yunfeng Yuan Ruru Gou Xiaonan Li School of Cyber Security and Computer Hebei UniversityHebei 071002China Hebei Machine Vision Engineering Research Center BaodingHebei 071002China College of Education Hebei UniversityHebei 071002China

As an important research topic in recent years,semantic segmentation has been widely applied to image understanding problems in various *** the successful application of deep learning methods in machine vision,the superior performance has been transferred to agricultural image processing by combining them with traditional *** segmentation methods have revolutionized the development of agricultural automation and are commonly used for crop cover and type analysis,pest and disease identification,*** frst give a review of the recent advances in traditional and deep learning methods for semantic segmentation of agricultural images according to different segmentation *** we introduce the traditional methods that can effectively utilize the original image information and the powerful performance of deep learningbased ***,we outline their applications in agricultural image *** our literature,we identify the challenges in agricultural image segmentation and summarize the innovative developments that address these *** robustness of the existing segmentation methods for processing complex images still needs to be improved urgently,and their generalization abilities are also *** particular,the limited number of labeled samples is a roadblock to new developed deep learning methods for their training and *** this,segmentation methods that augment the dataset or incorporate multimodal information enable deep learning methods to further improve the segmentation *** review provides a reference for the application of image semantic segmentation in the field of agricultural informatization.

关键词： Semantic segmentation Agricultural images Deep learning Convolution neural networks

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IPMV 2023 - Proceedings of 2023 5th International Conference on image processing and machine vision

IPMV 2023 - Proceedings of 2023 5th International Conference...

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5th International Conference on image processing and machine vision, IPMV 2023

ISBN: (纸本)9781450397926

The proceedings contain 16 papers. The topics discussed include: performance evaluation of recent object detection models for traffic safety applications on edge;tracking of artillery shell using optical flow;action recognition with non-uniform key frame selector;a view direction-driven approach for automatic room mapping in mixed reality;automatic gait gender classification using convolutional neural networks;deep 3D-2D convolutional neural networks combined with Mobinenetv2 for hyperspectral image classification;attention based BiGRU-2DCNN with hunger game search technique for low-resource document-level sentiment classification;strategies of multi-step-ahead forecasting for chaotic time series using autoencoder and LSTM neural networks: a comparative study;semi-supervised defect segmentation with uncertainty-aware pseudo-labels from multi-branch network;and security analysis of visual based share authentication and algorithms for invalid shares generation in malicious model.

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Review of surface defect detection of steel products based on machine vision

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IET image processing 2023年第2期17卷 303-322页

作者： Tang, Bo Chen, Li Sun, Wei Lin, Zhong-kang Wuhan Univ Sci & Technol Sch Machinery & Automat Wuhan 430081 Hubei Peoples R China Wuhan Univ Sci & Technol Sch Comp Sci & Technol Wuhan Hubei Peoples R China

Steel plays an important role in industry, and the surface defect detection for steel products based on machine vision has been widely used during the last two decades. This paper attempts to review state-of-art of vision-based surface defect inspection technology of steel products by investigating about 170 publications. This review covers the overall aspects of vision-based surface defect inspection for steel products including hardware system, automated vision-based inspection method, existing problems and latest development. The types of steel product surface defects composition of visual inspection system are briefly described, and image acquisition system is introduced as well. The image processing algorithms for surface defect detection of steel products are reviewed, including image pre-processing, region of interest (ROI) detection, image segmentation for ROI, feature extraction and selection and defect classification. The important problems such as small sample and real time of steel surface defect detection are discussed. Finally, the challenge and development trend of steel surface defect detection are prospected.

关键词： image segmentation feature extraction vision-based surface defect inspection technology image classification Industrial applications of IT production engineering computing quality control defect classification machine vision Optical, image and video signal processing steel products including hardware system Computer vision and image processing techniques steel Inspection and quality control image processing inspection review state-of-art visual inspection system Production engineering computing steel surface defect detection steel product surface defects composition computer vision automatic optical inspection reviews automated vision-based inspection method

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Hypercomplex Techniques in Signal and image processing Using Network Graph Theory: Identifying core research directions [Hypercomplex Signal and image processing]

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IEEE SIGNAL processing MAGAZINE 2024年第2期41卷 14-28页

作者： Alcayde, Alfredo Ventura, Jorge Montoya, Francisco G. Univ Almeria Dept Engn Almeria 04120 Spain Univ Almeria Fac Engn Almeria 04120 Spain

This article aims to identify core research directions and provide a comprehensive overview of major advancements in the field of hypercomplex signal and image processing techniques using network graph theory. The methodology employs community detection algorithms on research networks to uncover relationships among researchers and topic fields in the hypercomplex domain. This is accomplished through a comprehensive academic database search and metadata analysis from pertinent papers. The article focuses on the utility of these techniques in various applications and the value of mathematically rich frameworks. The results demonstrate how optimized network-based approaches can determine common topics and emerging lines of research. The article identifies distinct core research directions, including significant advancements in image/video processing, computer vision, signal processing, security, navigation, and machine learning within the hypercomplex domain. Current trends, challenges, opportunities, and the most promising directions in hypercomplex signal and image processing are highlighted based on a thorough literature analysis. This provides actionable insights for researchers to advance this domain.

关键词： Surveys Computer vision Technological innovation Navigation Algebra image processing Quaternions

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Integrated vision-based seam tracking system for robotic laser welding of curved closed square butt joints

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2025年第7-8期137卷 3387-3399页

作者： Nilsen, Morgan Sikstrom, Fredrik Univ West Dept Engn Sci S-46180 Trollhattan Sweden

This study presents a vision-based closed-loop tracking system designed specifically for robotic laser beam welding of curved and closed square butt joints. The proposed system is compared against 11 existing solutions reported in the literature, which employ various sensor principles for the same application. The system employs a non-contact, non-intrusive machine vision approach, seamlessly integrated into the laser beam welding head to mitigate challenges associated with sensor forerun. Key features include an off-axis LED illumination, an optical filter, and a movable actuator, facilitating real-time image processing and closed-loop control during the welding process. Experimental validation was conducted on stainless-steel plates with complex closed square butt joints. The system achieved a mean absolute joint-to-beam offset of 0.14 mm across four test cases, with a maximum offset of 0.85 mm, demonstrating its robustness and precision. Comparative analysis underscores the proposed method's advantages, showcasing its potential for industrial applications in laser beam welding of geometrically challenging joints.

关键词： Robotic laser welding Seam tracking machine vision image processing Non-contact sensing Automatic control

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