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21st International Conference on image Analysis and processing (ICIAP)

作者： Montoro-Lendinez, Alicia Diaz-Jimenez, David Luis Lopez-Ruiz, Jose Medina-Quero, Javier Espinilla-Estevez, Macarena Univ Jaen Dept Comp Sci Jaen 23071 Spain

ISBN: (纸本)9783031133213;9783031133206

Among the instruments for early detection are those for analysing gases in people's faeces, as it has been found that the presence of the intensity of certain compounds is related to the presence of cancer, diabetes or Alzheimer. The availability of sensor devices in recent years, together with the Internet of Things (IoT) paradigm, has made it possible to create low-cost systems that allow initial solutions to be tested for various real applications. Therefore, the aim of this contribution is to present the use case of a stool gas monitoring system in order to be the beginning of a solution for the early detection of this type of diseases. The proposed prototype integrates a thermal camera and MOX sensors to collect temperature and gas measurements immediately after a person has made deposition in their home. The measurements are monitored through an IoT platform and stored on a cloud server.

关键词： Stool monitoring system Volatile organic compounds (VOCs) MOX sensors Thermal camera Internet of Things (IoT)

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A review of machine vision technology for defect detection in curved ceramic materials

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NONDESTRUCTIVE TESTING AND EVALUATION 2024年

作者： Dong, Guanping Pan, Xingcheng Liu, Sai Wu, Nanshou Kong, Xiangyu Huang, Pingnan Wang, Zixi Jingdezhen Ceram Univ Sch Mech & Elect Engn Jingdezhen Peoples R China South China Univ Technol State Key Lab Luminescent Mat & Devices Guangzhou Peoples R China South China Univ Technol Sch Automat Sci & Engn Guangzhou Peoples R China Guangdong Polytech Sci & Trade Inst Informat Guangzhou Peoples R China Foshan Univ Sch Mechatron Engn & Automat Foshan Peoples R China Tsinghua Univ Dept Mech Engn State Key Lab Tribol Adv Equipment Beijing Peoples R China

Curved ceramic materials possess corrosion-resistant, heat-resistant, and exceptional hardness, rendering them indispensable in many critical fields, including automotive, aerospace, biomedical, architectural, and artistic applications. The detection of defects in these materials is of paramount importance;but traditional inspection methods fall short due to their unique structural characteristics. Presently, the assessment of curved ceramics predominantly relies on manual inspection, which is fraught with subjective errors and inefficiencies. This paper provides a comprehensive overview of the advancements in machine vision technology as applied to defect detection across various types of curved ceramic materials, including industrial, daily-use, and artistic ceramics. The paper highlights the key factors influencing the development of surface defect detection technology in curved ceramics. It summarises the advantages and disadvantages of traditional machine vision and deep learning approaches for defect detection in curved ceramics and analyzes their respective application scopes. Concluding with an examination of the technical challenges inherent in defect detection, the paper also outlines a prospective trajectory for the advancement of inspection technology in curved ceramic materials.

关键词： Surface ceramics machine vision image processing defect detection review

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A Systematic Survey of Automatic image Description Generation Systems

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INTERNATIONAL JOURNAL OF image AND GRAPHICS 2024年第0期

作者： Sreela, S. R. Idicula, Sumam Mary Govt Coll Dept Comp Sci Thiruvananthapuram Kerala India Muthoot Inst Technol & Sci Dept Comp Sci Ernakulam Kerala India

Automatic image description generation is an active research area in Computer vision and Natural Language processing. Objects, their attributes, actions, and spatial relationships are identified in the image description generation system. Earlier, these systems used classical machine learning approaches. Later majority of these works follow deep learning strategies. The essential goal of these systems is to produce syntactically and semantically correct sentences. This review aims to synthesize the studies conducted from 2010 to 2023 to get a deeper view of various image description generation systems and their applications. The prominent contribution of this review is that it covers the different aspects of image captioning systems, such as the methods used, the various applied domains, evaluation measures, and the datasets used. A single synthesized study directs scholars regarding developing image captioning systems to date utilizing machine learning approaches. It also offers suggestions for researchers in this sector for the future. image captioning is applied in many fields like natural images, medical images, remote sensing images, videos, etc. This review paper reviews the various taxonomies of image description generation systems. We also analyzed multiple methods used in the architecture of image captioning systems. The datasets and the evaluation metrics used in these systems are discussed. We studied the performance of the system under various circumstances.

关键词： Automatic image description generation image captioning medical image captioning

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Joint Visual-Textual Modeling for Multimodal Content Classification, Retrieval and Generation

Joint Visual-Textual Modeling for Multimodal Content Classif...

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作者： Chen, Tianlang University of Rochester

学位级别：Ph.D., Doctor of Philosophy

With the development of computer vision, natural language processing, and machine learning technologies, a great number of joint visual-textual applications, such as image captioning, visual question answering, visual grounding, image-text cross-modal retrieval, and text-based image generation, emerged in recent years. They leverage machine learning models as the core module to tackle problems related to the intersection of vision and language. For all these joint visual-textual applications, vision and text modalities interact in three fundamental modes. The first is the "joint learning" mode, which considers both modalities as parallel inputs to jointly predict a target. The second is the "retrieval" mode, which explores the correspondence relation between the two modalities and aims to find the corresponding items that belong to different modalities. The third is the "generation" mode, which focuses on creating and modifying the items of one modality using the input of another modality as guidance. For all the joint visual-textual applications of the three modes, how to effectively "capture" and "attend" to the significant information of the visual and textual inputs is crucial. This thesis develops new "capturing" and "attending" methods to effectively model joint visual-textual applications in the three modes. For the first mode, we focus on a significant social media classification application. A novel bilateral attention model is proposed to classify whether a WeChat Moment is related to business or not based on the Moment's image and text information. For the second mode, we comprehensively investigate the application of image-text cross-modal retrieval on both general and domain-specific tasks. We first explore the general image-text matching task and propose approaches that capture high-performance cross-modal information. We then focus on two domain-specific tasks related to font retrieval and person search. We design methods to further utilize the specia

关键词： Computer vision Joint visual-textual application Joint visual-textual modeling Natural language processing

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Keypoint-Based Foreground-Background image Segmentation

Keypoint-Based Foreground-Background Image Segmentation

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International Symposium on Electronics in Marine (ELMAR)

作者： Jaroslav Venjarski L'uboš Likó Šimon Tibenský Marek Vančo Gregor Rozinaj Institute of Multimedia ICT Slovak University of Technology in Bratislava Slovakia

ISBN: (数字)9798350375428

ISBN: (纸本)9798350375435

This article centers on foreground-background image segmentation techniques, with a particular focus on applications using two shifted or highly similar images. Our work critically examines prevalent image processing methodologies, leading to the development and implementation of an innovative algorithm for more precise segmentation. We initially unpack the concepts of image segmentation and stereo vision, setting the stage for an in-depth exploration of various segmentation methods and techniques. Our investigation reveals that synergistic combinations of methods frequently produce more refined outcomes. We subsequently propose a practical solution, juxtaposing our approach with pre-existing alternatives to delineate its comparative strengths, weaknesses, and unique attributes.

关键词： Photography Deep learning image segmentation Visualization machine learning algorithms Accuracy image edge detection Production Real-time systems Stereo vision

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Traffic Management System Using Computer vision 5th

Traffic Management System Using Computer Vision

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5th International Conference on Recent Trends in machine Learning, IoT, Smart Cities, and applications, ICMISC 2024

作者： Jeslin Shanthamalar, J. Yuthika, P. Kannan, R. Albert Mayan, J. Velvizhi, R. Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Tamil Nadu Chennai600119 India

ISBN: (纸本)9789819788644

It is essential to find creative solutions to the growing urban problems of traffic congestion and parking issues. By using real-time traffic camera photos with image processing and deep learning algorithms to compute traffic density in real-time, the suggested approach gets around these problems. The system speeds up transit and lowers pollution in megacities by reducing traffic dramatically and dynamically modifying traffic lights based on vehicle density. The study also presents a novel solution to the issue of people circling open lots in search of parking spots. The system achieves a remarkable positive detection rate by using thermal cameras and cutting-edge deep learning architectures using Yolov4. The overall accuracy of the YOLO v4 model in this hypothetical scenario is approximately 0.90. Parking lot cruising patterns are illuminated by the precise tracking of several moving automobiles made possible by Kalman filters. In addition to improving traffic control, this combination of state-of-the-art technologies provides insightful information about parking behavior, opening the door to more effective urban mobility solutions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Traffic congestion

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Hierarchical Design of a Secure image Sensor with Dynamic Reconfiguration

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JOURNAL OF SIGNAL processing SYSTEMS FOR SIGNAL image AND VIDEO TECHNOLOGY 2020年第9期92卷 999-1015页

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

This paper presents a secure reconfigurable hierarchical hardware architecture at the pixel and region level for smart image sensors to accelerate machine vision applications. The design maintains hierarchical processing that begins at the pixel level. It aims to reduce the computational burden on the sequential processor and increases the confidentiality of the sensor. We achieve this goal by preprocessing the data in parallel with event-based processing within the sensor and extract the local features, which are then forwarded to an encryption module. After that, an external processor can obtain the encrypted features to complete the vision application. This approach significantly accelerates the vision application by executing the low-level and mid-level image processing applications and simultaneously by reducing the data volume at the sensor level. The secure hardware architecture enables the vision application to perform in real-time with reliability. This hierarchical processing breaks the traditional sequential image processing and introduces parallelism for machine vision applications. We evaluate the design in FPGA and achieve the GDSII file in the ASIC platform at 800MHz. Simulation results show that the area overhead and power penalty for adding reconfiguration features stay in an acceptable range. Besides, removing redundant information, 84.01%, and 94.31% dynamic power can be saved at each pixel-level and region-level, respectively.

关键词： Pixel level processing Reconfigurability Predictive coding Attention module image encryption Secure image sensor FPGA ASIC

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Product Digital Quality Inspection using machine vision Systems - A Categorical Review 17

Product Digital Quality Inspection using Machine Vision Syst...

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17th Annual System of Systems Engineering Conference, SOSE 2022

作者： Reyna, Mark Delgado, Guadalupe Akundi, Aditya Luna, Sergio Chumacero, Erik University of Texas Rio Grande Valley Complex Engineering Systems Laboratory BrownsvilleTX United States University of Texas Rio Grande Valley Dept. of Manufacturing and Industrial Engineering EdinburgTX United States University of Texas Rio Grande Valley Dept. of Informatics and Engineering Systems BrownsvilleTX United States University of Texas at El Paso El Industrial Manufacturing and Systems Engineering Dept El PasoTX United States

ISBN: (纸本)9781665496230

A key element of quality control in manufacturing is Product Inspection, which is a process that allows for verifying a product's quality enabled by activities such as measuring, examining, and testing one or more characteristics of a product. Automated machine vision systems undertake this task for in-line, in-process inspections. machine vision (MV) technology is designed to imitate the human visual system. The ability for computers to see the world through cameras and analyze their findings autonomously, often through deep learning, is making MV a market-disrupting technology. Many manufacturers seek to adopt this technology to improve production rates, product quality, and lower costs. The focus of this paper is to review the MV systems being developed to handle quality control, specifically product inspection tasks for various manufacturing applications. We present recently developed MV systems that perform product inspection based on free-form analysis, dimensional analysis, and surface analysis. Further, a discussion is provided to address the commonalities found in MV systems and the challenges the technologies continue to face. Additionally, each product quality inspection technique is examined for image processing techniques used. © 2022 IEEE.

关键词： Computer vision

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CD-SEM Anomaly Detection Through machine Learning and Generative AI

CD-SEM Anomaly Detection Through Machine Learning and Genera...

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IEEE/SEMI Conference and Workshop on Advanced Semiconductor Manufacturing

作者： Susan Sun Xiaoyan Zhuo Phong Nguyen Todd Smith Jinhua Ding Troy Stansell Amit Srivastava Yousef Hanif Data Science Micron Technology Inc. Manassas VA USA Lithography Process Eng Micron Technology Inc. Manassas VA USA Data Science Micron Technology Inc. Singapore Product Quality Assurance Micron Technology Inc. Manassas VA USA

ISBN: (数字)9798331531850

ISBN: (纸本)9798331531867

In a complex semiconductor manufacturing environment, critical dimension scanning electron microscope (CD-SEM) images are captured at metrology step to monitor structural measurements and detect anomaly to meet the stringent process control requirements. This paper focuses on advanced CD-SEM image processing and anomaly detection using machine learning and generative AI models, which include computer vision (CV) image processing, Residual Neural Network (ResNet) deep learning and Generative Adversarial Network (GAN) model for various use cases. The applications of these models during in-line monitoring are crucial to identify potential process issues for yield and quality improvement.

关键词： Deep learning Semiconductor device measurement Generative AI image processing Computational modeling Semiconductor device manufacture Generative adversarial networks Servers Anomaly detection Monitoring

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Colour based Object Classification using KNN Algorithm for Industrial applications 1

Colour based Object Classification using KNN Algorithm for I...

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1st IEEE International Conference on Automation, Computing and Renewable Systems, ICACRS 2022

作者： Santosh Kumar, N.C. Uma Maheswari, S. Vigneshwari, M. Pramila, P.V. Khilar, Rashmita Kumar, Ashok Department of Computer Science Engineering Kakatiya Institute of Technology and Science Telangana Warangal India Department of Electronics and Communication Engineering Panimalar Engineering College India Department of Information Technology Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Tamil Nadu Chennai India Department of Computer Science and Engineering Saveetha Schoolof Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai India Department of Information Technology Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai India Department of Computer Science Banasthali Vidyapith Rajasthan India

ISBN: (纸本)9781665460842

Object classification and detection involve numerous applications like image processing, picture retrieval, security and surveillance, video communication, robot vision and observation. They are often classified based on their properties like colour, shape, quality and texture. However, their accuracy is mostly predicted by colour recognition. Colour based detection of objects has a discriminating quality to detect an object of any primary colour. The extraction of two or three-dimensional objects based on colour plays a vital role in real-time image processing technology. This review paper proposes the method for colour-based object classification applied in the lays industry using the 'K-nearest neighbour algorithm' which is the simplest form of machine learning classification method. The ML model is designed in such a way that once the colour of the lays pack is detected it also specifies its particular flavour. Hence for efficient machine-based colour identification in industries, this method would be an appropriate one and can be used for many other applications. © 2022 IEEE

关键词： Classification (of information)

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