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Algorithm, image processing and machine vision (AIPMV), International Conference on

作者： Bingxin Zha Huijie Zhu Haolin Zhang Shengying Yang School of Information and Electronic Engineering Zhejiang University of Science and Technology Hangzhou China Huzhou Zhongke Fanzai Electric Power Technology Development Co. Ltd Huzhou China Arm Technology (China) Co. Ltd Shanghai China

ISBN: (数字)9798350390254

ISBN: (纸本)9798350390261

In the field of computer vision, the task of facial super-resolution (FSR) is crucial for applications such as surveillance and photo restoration. However, factors such as noise and artifacts in real-world scenarios severely degrade image quality. Although existing methods use geometric priors and facial heatmap localization to improve FSR performance, these priors are often inaccurate under low-light conditions, affecting the results. This paper proposes an innovative low-light facial enhancement network aimed at integrating FSR and low-light enhancement tasks. We designed a Light Frequency Inference Block (LFIB) to capture and refine brightness and texture features at different frequencies, combining it with Transformer modules in an Encoder-Decoder architecture. The LFIB module separates degraded images into low-frequency and high-frequency brightness features and employs spatial cross-attention to capture facial texture details, effectively addressing the degradation issues in facial images. Experimental results demonstrate that our method excels in the low-light facial super-resolution task, outperforming existing methods on various metrics. Additionally, it shows good generalization capabilities in real-world scenarios, confirming its potential for practical applications.

关键词： Measurement image quality Visualization Superresolution Brightness Computer architecture Transformers Feature extraction image restoration Faces

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Control of Technical Parameters of Industrial Products Through machine vision 43rd

Control of Technical Parameters of Industrial Products Throu...

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43rd IBIMA Conference on Artificial intelligence and machine Learning, IBIMA-AI 2024

作者： Gheorghita, Vlad National University of Science and Technology POLITEHNICA Bucharest Romania

ISBN: (纸本)9783031774928

During the production process, a lot of undesirable or faulty products are also created. Since these products cannot be used or distributed on the market, they must be discarded. Product integrity and quality inspection, which is also a result of production process efficiency and optimization, is one of the most important procedures in modern industrial applications. The quickest and most efficient way to make sure products meet the quality requirements is through an automated verification process. These systems are considered as essential by some applications because replacing them with human employees would necessitate a substantial workforce and related costs. This research provides a mechanical experimental system that will be able to verify fast and control the most important parameters of various products using the artificial intelligence approach and machine vision techniques. The cost of the designed system is low and the structure used is clear. The demonstration of the utility of the created product was carried out on a toothed wheel with many essential technical characteristics, a widely used landmark in the field of manufacturing engineering. image processing techniques are used such as threshold function, calibration, pattern matching, measure. The experimental model designed is easy to implement and can represent a transition towards the application of modern industry 5.0 principles in different processing companies. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： machine vision

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Maritime image Stabilization: A Comprehensive Review of Techniques and Challenges

Maritime Image Stabilization: A Comprehensive Review of Tech...

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Electronic Technology and Information Science (ICETIS), International Conference on

作者： Enping Wei Yong Chai Tan Vin Cent Tai Yanan Hao Xiaodong Zhang Tian Zhang Centre for Modelling and Simulation Faculty of Engineering Built Environment and Information Technology SEGI University Kuala Lumpur Malaysia

ISBN: (数字)9798350388343

ISBN: (纸本)9798350388350

image stabilization plays a crucial role in providing accurate and reliable visual information for machine vision applications. In maritime applications, such as unmanned ship navigation, where six degrees of freedom (DOF) motion and harsh maritime conditions prevail, the efficacy of image stabilization technology is vital for robust image processing algorithms. This paper offers a comprehensive review of image stabilization techniques tailored for maritime environments, developed over the past two decades. We analyzed a total of 39 research articles on the subject, sourced from Web-of-Science, SCOPUS, and the Engineering Index databases, discussing potential research directions to address the limitations of current image stabilization methods, with special consideration for the unique requirements of ship-borne cameras. It provides an up-to-date overview of the techniques, limitations, and algorithms of ship-borne cameras for maritime applications, identifying current knowledge gaps and areas requiring further research. This review aims to guide the development of new technologies and methods to improve the performance of image stabilization systems in maritime contexts.

关键词： Vibrations Visualization Reviews Target recognition machine vision Stability criteria Object detection

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The Evolution and Application of Artificial Intelligence in Rhinology: A State of the Art Review

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OTOLARYNGOLOGY-HEAD AND NECK SURGERY 2023年第1期169卷 21-30页

作者： Amanian, Ameen Heffernan, Austin Ishii, Masaru Creighton, Francis X. Thamboo, Andrew Univ British Columbia Dept Surg Div Otolaryngol Head & Neck Surg Vancouver BC Canada Johns Hopkins Univ Sch Med Dept Otolaryngol Head & Neck Surg Baltimore MD 21205 USA

Objective To provide a comprehensive overview on the applications of artificial intelligence (AI) in rhinology, highlight its limitations, and propose strategies for its integration into surgical practice. Data Sources Medline, Embase, CENTRAL, Ei Compendex, IEEE, and Web of Science. Review Methods English studies from inception until January 2022 and those focusing on any application of AI in rhinology were included. Study selection was independently performed by 2 authors;discrepancies were resolved by the senior author. Studies were categorized by rhinology theme, and data collection comprised type of AI utilized, sample size, and outcomes, including accuracy and precision among others. Conclusions An overall 5435 articles were identified. Following abstract and title screening, 130 articles underwent full-text review, and 59 articles were selected for analysis. Eleven studies were from the gray literature. Articles were stratified into image processing, segmentation, and diagnostics (n = 27);rhinosinusitis classification (n = 14);treatment and disease outcome prediction (n = 8);optimizing surgical navigation and phase assessment (n = 3);robotic surgery (n = 2);olfactory dysfunction (n = 2);and diagnosis of allergic rhinitis (n = 3). Most AI studies were published from 2016 onward (n = 45). Implications for Practice This state of the art review aimed to highlight the increasing applications of AI in rhinology. Next steps will entail multidisciplinary collaboration to ensure data integrity, ongoing validation of AI algorithms, and integration into clinical practice. Future research should be tailored at the interplay of AI with robotics and surgical education.

关键词： artificial intelligence rhinology machine learning computer vision prediction prognosis

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Self-Supervised Deep Fisheye image Rectification Approach using Coordinate Relations 17

Self-Supervised Deep Fisheye Image Rectification Approach us...

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17th International Conference on machine vision applications (MVA)

作者： Hosono, Masaki Simo-Serra, Edgar Sonoda, Tomonari Waseda Univ Tokyo Japan Utagoe Inc Tokyo Japan

ISBN: (纸本)9784901122207

With the ascent of wearable camera, dashcam, and autonomous vehicle technology, fisheye lens cameras are becoming more widespread. Unlike regular cameras, the videos and images taken with fisheye lens suffer from significant lens distortion, thus having detrimental effects on image processing algorithms. When the camera parameters are known, it is straight-forward to correct the distortion, however, without known camera parameters, distortion correction becomes a non-trivial task. While learning-based approaches exist, they rely on complex datasets and have limited generalization. In this work, we propose a CNN-based approach that can be trained with readily available data. We exploit the fact that relationships between pixel coordinates remain stable after homogeneous distortions to design an efficient rectification model. Experiments performed on the cityscapes dataset show the effectiveness of our approach. Our code is available at GitHub(1).

关键词： machine vision Distortion Cameras Data models Task analysis image reconstruction Autonomous vehicles

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AI-powered end-to-end product lifecycle: UX-centric human-in-the-loop system boosting reviewer productivity by 82% and accelerating decision-making via real-time anomaly detection and data refinement with GPU-accelerated computer vision, edge computing, and scalable cloud

AI-powered end-to-end product lifecycle: UX-centric human-in...

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SPIE Advanced Lithography + Patterning

作者： Kiarash Ahi Sherif Mansour Stewart Wu Germain Fenger Anirudh Shreekantha Ayya Juli Opitz Nacer Zine El Abidine Hosam Hatem Amr Essam Ahmed El Dessouki Satya Sriram Sayani Biswas Samir Bhamidipati Mark Pereira Mennat-Allah Fekry Rana Fawzi Hesham Samir Muhammed Damsas Nalakath Omar Mabrouk Siemens EDA (United States) Siemens EDA (Egypt) Siemens EDA (Belgium) Siemens EDA (India) Siemens EDA (France)

ISBN: (纸本)9781510686380

As the amount of data continues to grow, with images becoming larger in terms of bytes and details across scientific, photography, industrial, medical, gaming, augmented reality (AR), virtual reality (VR), autonomous systems, and digital content creation domains, and as generative AI (Gen AI) and large language models (LLMs) gain popularity, the computational demands on modern systems have reached unprecedented levels. In this work, we introduce optimized, GPU-parallelized image processing algorithms that significantly reduce computation requirements, improving both processing speed and scalability. Additionally, in our work, edge computing plays a critical role in reducing latency and bandwidth consumption, enabling real-time decision-making by processing data at the edge, closer to the source. Scalable cloud architectures further support this approach by providing elastic, high-throughput processing capabilities for data-intensive tasks. Moreover, in many applications such as medical imaging, autonomous vehicle perception, satellite image analysis, high-precision manufacturing, robotics, and real-time anomaly detection, image review by humans is not only error-prone but also time-consuming, often limiting scalability and response times. To address this, we present a human-in-the-loop (HITL) automation framework, coupled with a UX-centric, GUI-based system, that reduces human workload by 82%, making high-throughput image review feasible even at the scale required for cutting-edge applications like medical imaging, digital marketplaces, platform integrity, semiconductor manufacturing, autonomous systems, precision agriculture, and industrial defect detection. This framework leverages GPU-accelerated computer vision, edge computing, and scalable cloud infrastructure to optimize data processing across the entire cycle, from initial capture to real-time analysis and decision-making. To demonstrate this approach, we utilize challenging images characterized by low signal

关键词： Artificial intelligence Computer vision technology Data analysis Defect detection Defect inspection Human-machine interfaces image analysis image processing image quality Line edge roughness machine learning machine vision Modeling Scanning electron microscopy Windows

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Unifying image processing as visual prompting question answering 24

Unifying image processing as visual prompting question answe...

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Proceedings of the 41st International Conference on machine Learning

作者： Yihao Liu Xiangyu Chen Xianzheng Ma Xintao Wang Jiantao Zhou Yu Qiao Chao Dong Shanghai Artificial Intelligence Laboratory and Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shanghai Artificial Intelligence Laboratory and Shenzhen Institute of Advanced Technology Chinese Academy of Sciences and University of Macau Shanghai Artificial Intelligence Laboratory Kuaishou Technology University of Macau Shenzhen Institute of Advanced Technology Chinese Academy of Sciences

image processing is a fundamental task in computer vision, which aims at enhancing image quality and extracting essential features for subsequent vision applications. Traditionally, task-specific models are developed for individual tasks and designing such models requires distinct expertise. Building upon the success of large language models (LLMs) in natural language processing (NLP), there is a similar trend in computer vision, which focuses on developing large-scale models through pretraining and in-context learning. This paradigm shift reduces the reliance on task-specific models, yielding a powerful unified model to deal with various tasks. However, these advances have predominantly concentrated on high-level vision tasks, with less attention paid to low-level vision tasks. To address this issue, we propose a universal model for general image processing that covers image restoration, image enhancement, image feature extraction tasks, etc. Our proposed framework, named PromptGIP, unifies these diverse image processing tasks within a universal framework. Inspired by NLP question answering (QA) techniques, we employ a visual prompting question answering paradigm. Specifically, we treat the input-output image pair as a structured question-answer sentence, thereby reprogramming the image processing task as a prompting QA problem. PromptGIP can undertake diverse cross-domain tasks using provided visual prompts, eliminating the need for task-specific finetuning. Capable of handling up to 15 different image processing tasks, PromptGIP represents a versatile and adaptive approach to general image processing. Codes will be available at https://***/lyh-18/PromptGIP.

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Automatic methods for classification of visual based viral and bacterial disease symptoms in plants

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International Journal of Information Technology (Singapore) 2022年第1期14卷 287-299页

作者： Yakkundimath, Rajesh Saunshi, Girish Palaiah, Surendra Department of Computer Science and Engineering K. L. E. Institute of Technology Karnataka Hubballi 580030 India Department of Genetics and Plant Breeding University of Agricultural Sciences Karnataka Dharwar 580002 India Visvesvaraya Technological University Karnataka Belagavi 590018 India

In this research, machine vision system is developed for the automatic identification of plant disease symptoms, from color images. Visual symptoms based identification of viral and bacterial diseases in plants using image processing and pattern classification is presented. Viral and bacterial plant disease symptoms affecting leaf, stem and fruit are segmented and features are selected based on co-occurrence based multispectral approach and co-occurrence based color moments approach. The artificial neural network (ANN), support vector machine (SVM) and convolutional neural network (CNN) are deployed for image-based disease symptoms classification. The maximum mean classification result of 90.72% is achieved using co-occurrence based color moments approach with SVM, on the held-out dataset comprising 4000 images of 20 plant disease symptoms. The method developed has shown applications in building intelligent systems which can be used automatically to identify the visual symptoms of plant disease and assist farmers. © 2021, Bharati Vidyapeeth's Institute of Computer applications and Management.

关键词： Classifiers Co-occurrence based methods image analysis Plant disease

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Research on Real-Time 3D image Acquisition System Based on Stripe Projection

Research on Real-Time 3D Image Acquisition System Based on S...

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image processing, Computer vision and machine Learning (ICICML), International Conference on

作者： Duo Zhou Shibin Yin Hongbo Xie School of Precision Instrument and Opto-Electronics Engineering Tianjin University Tianjin China Isvision (Hangzhou) Technology Company Limited Hangzhou China

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

In response to the demand for rapid acquisition of three-dimensional profiles in industrial online inspection, a real-time 3D image acquisition system has been designed and researched. This system is based on the principles of optical projection and binocular vision fusion, utilizing a DLP projector and industrial cameras to construct a hardware platform, along with the development of an efficient 3D reconstruction algorithm. A three-frequency phase-shifting coding scheme is employed in the stripe pattern design, and a phase unwrapping algorithm guided by a quality map is proposed to enhance reconstruction reliability. The system achieves parallel processing through a CUDA heterogeneous computing platform and establishes a four-stage pipeline structure to optimize real-time performance. Experimental results show that the system meets the expected goals in key metrics such as spatial resolution, measurement accuracy, and real-time performance. In practical applications on the mobile phone casing inspection production line, the system demonstrates superior detection efficiency and reliability compared to traditional methods. The research results provide a complete solution for rapid 3D inspection, holding significant engineering application value in fields like industrial quality inspection and reverse engineering.

关键词： Three-dimensional displays Production Inspection Parallel processing Optical imaging Real-time systems Time measurement Heterogeneous networks Spatial resolution image reconstruction

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A view of computational models for image segmentation

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Annali dell'Universita di Ferrara 2022年第2期68卷 277-294页

作者： Antonelli, Laura De Simone, Valentina di Serafino, Daniela Institute for High Performance Computing and Networking (ICAR) CNR Via Pietro Castellino 111 Napoli 80131 Italy Department of Mathematics and Physics University of Campania “Luigi Vanvitelli” viale Abramo Lincoln 5 Caserta 81100 Italy Department of Mathematics and Applications “R. Caccioppoli” University of Naples Federico II Via Cintia Monte S. Angelo Napoli 80126 Italy

image segmentation is a central topic in image processing and computer vision and a key issue in many applications, e.g., in medical imaging, microscopy, document analysis and remote sensing. According to the human perception, image segmentation is the process of dividing an image into non-overlapping regions. These regions, which may correspond, e.g., to different objects, are fundamental for the correct interpretation and classification of the scene represented by the image. The division into regions is not unique, but it depends on the application, i.e., it must be driven by the final goal of the segmentation and hence by the most significant features with respect to that goal. Thus, image segmentation can be regarded as a strongly ill-posed problem. A classical approach to deal with ill posedness consists in incorporating in the model a-priori information about the solution, e.g., in the form of penalty terms. In this work we provide a brief overview of basic computational models for image segmentation, focusing on edge-based and region-based variational models, as well as on statistical and machine-learning approaches. We also sketch numerical methods that are applied in computing solutions to these models. In our opinion, our view can help the readers identify suitable classes of methods for solving their specific problems. © 2022, The Author(s).

关键词： Ill-posed problems image segmentation machine-learning Numerical optimization

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