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检索条件"任意字段=Iranian Conference on Machine Vision and Image Processing"

共 11454 条记录，以下是101-110 订阅

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A NEEDLE IN A (MEDICAL) HAYSTACK: DETECTING A BIOPSY NEEDLE IN ULTRASOUND imageS USING vision TRANSFORMERS 31

A NEEDLE IN A (MEDICAL) HAYSTACK: DETECTING A BIOPSY NEEDLE ...

引用

2024 International conference on image processing

作者： Wijata, Agata M. Pycinski, Bartlomiej Nalepa, Jakub Silesian Tech Univ Akad 2A PL-44100 Gliwice Poland Maria Sklodowska Curie Natl Res Inst Oncol Gliwice Branch Tumor Pathol Dept Wybrzeze Armii Krajowej 15 PL-44102 Gliwice Poland

ISBN: (纸本)9798350349405;9798350349399

Needle localization in ultrasound images is pivotal for the successful execution of ultrasound-guided core needle biopsies. Automating the needle detection process can decrease the procedure time and lead to a more precise diagnosis. In this article, we introduce an automatic method for detecting the core needle and determining its trajectory in 2D ultrasound images. In our approach, the vision Transformer architecture, renowned for its self-attention mechanisms is used for needle detection and segmentation, and is followed by the analysis of the Radon transformed segmentation mask to identify the needle's trajectory. The experiments, performed over two clinical datasets of more than 600 ultrasound images rigorously split into various training-test subsets and backed up with a variety of statistical analyses revealed that our approach offers high-quality needle segmentation, and significantly outperforms other techniques in identifying the needle's trajectory, with the trajectory localization errors reduced up to more than 5x when compared to the most competitive deep learning algorithm. We believe that our work may pave the way for more accurate and efficient ultrasound-guided procedures, ultimately improving patient outcomes.

关键词： Core needle biopsy cancer machine learning deep learning vision transformer ultrasound imaging

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A Secure Hybrid Permissioned Blockchain and Deep Learning Platform for CT image Classification 12

A Secure Hybrid Permissioned Blockchain and Deep Learning Pl...

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12th iranian/2nd International conference on machine vision and image processing, MVIP 2022

作者： Noei, Mohammadreza Parvizimosaed, Mohammadreza Saleh Bigdeli, Aliakbar Yalpanian, Mohammadmostafa Tarbiat Modares University Department of Electrical and Computer Engineering Tehran Iran K. N. Toosi University of Technology Department Computer Engineering Tehran Iran TisEnergy Network Engineer Tehran Iran

ISBN: (纸本)9781665412162

Pneumonia is a life-threatening and prevalent disease and needs to be diagnosed within a short time because of the lungs' fluid flow. Late detection of the disease may result in the patient's death. Thus, advanced diagnosis is a critical factor besides the disease progress. In addition to advanced diagnosis, the privacy of datasets is important for organizations. Due to the great value of datasets, hospitals do not want to share their datasets, but they want to share their trained network weights. Therefore, in this paper, we combine deep learning and blockchain to implement the blockchain as distributed storage. Using permission blockchain, weights are broadcasted among other hospitals securely. Because of the security, the dataset is shared with five hospitals equally. Each hospital trains its network model and sends its weights to the blockchain. The goal is to broadcast the aggregated weights among hospitals securely and have good enough results because the whole dataset is not implemented to train a network. The dataset contains 5856 images, and hospitals implement a residual neural network with 28 layers. The results show that hospitals can increase the accuracy of their model using shared weights compared to a model without using shared weights. © 2022 IEEE.

关键词： Blockchain

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Research on key technologies of intelligent vision system of industrial robot

Research on key technologies of intelligent vision system of...

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2024 International conference on Physics, Photonics, and Optical Engineering, ICPPOE 2024

作者： Yang, Libo Guangdong University of Science & Technology Dongguan523083 China

ISBN: (数字)9781510689121

ISBN: (纸本)9781510689114

machine vision allows industrial robots to acquire the same ability to see things as humans. The integrated machine vision system based on the vision sensor to obtain the image of the target object in the work scene, through the intelligent processing unit to simulate the extraction and analysis of important information of the human brain, to realize the identification, positioning and understanding of the target object, so as to improve the adaptability of industrial robots to the external environment, improve their perception and decision-making ability of complex industrial production environment. Through the research of this project, it will help promote the transformation of machine vision from low-level vision to high-level vision, further improve the intelligence level of industrial robots, improve the intelligence level of industrial robots in the industrial production and manufacturing process, and provide decision support for the research and development of intelligent industrial robots. © 2025 SPIE.

关键词： machine vision

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Advanced machine vision techniques for real-time quality detection and grading of navel oranges using high-resolution imaging and deep learning algorithms

Advanced machine vision techniques for real-time quality det...

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International conference on Computer vision, Robotics, and Automation Engineering, CRAE 2024

作者： Zhang, Xiaoyan Beijing Institute of Technology Zhuhai Zhuhai519000 China

ISBN: (纸本)9781510682283

In recent years, the rapid development of computer vision and artificial intelligence has significantly advanced agricultural applications, particularly in the quality detection and grading of navel oranges. This review explores the latest advancements in machine vision technologies for navel orange quality assessment, focusing on image processing, deep learning algorithms, and multispectral imaging. image processing techniques, fundamental to machine vision systems, enable the extraction of visual features such as color, shape, and texture, enhancing detection accuracy. Recent studies have demonstrated the efficacy of deep learning algorithms, particularly convolutional neural networks (CNNs), in achieving high-precision grading and real-time performance. Furthermore, multispectral and hyperspectral imaging technologies offer rich spectral information, facilitating more accurate quality detection and maturity assessment. Despite significant progress, challenges such as complex natural environments, lighting conditions, and the high cost of imaging equipment persist. Future research directions include integrating multi-source data fusion, developing efficient deep learning algorithms, and promoting cost-effective imaging technologies. Our proposed advanced algorithm employs the DAHENG machine vision experimental platform and MER-132-30UC camera for high-resolution image acquisition, with HALCON software for sophisticated image processing and real-time classification. This system enhances accuracy and efficiency in navel orange quality detection and grading, addressing key challenges in current agricultural practices. © 2024 SPIE.

关键词： Citrus fruits

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machine vision Intelligence Using Layer-Wise Relevance Backward Propagation For Breast Cancer Diagnosis 5

Machine Vision Intelligence Using Layer-Wise Relevance Backw...

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5th International conference on image processing and Capsule Networks, ICIPCN 2024

作者： Manuela, Ukech Melissa Nakasi, Rose Jjingo, Daudi Hellen, Nakayiza Ngobye, Martin Marvin, Ggaliwango Makerere University Department of Computer Science Kampala Uganda Makerere University Department of Information Techonology Kampala Uganda Muni University Department of Computer and Information Science Arua Uganda

ISBN: (纸本)9798350367171

machine vision intelligence with layer-wise relevance propagation (LRP) for breast cancer diagnosis is an innovative approach aimed at improving diagnostic accuracy and efficiency. Traditional methods for diagnosing breast cancer often rely on the manual interpretation of medical images, which can be time-consuming and prone to human error. This study proposes the development of a machine vision model utilising LRP designed to analyse mammographic images for the diagnosis of breast cancer. Various machine learning models, including Convolutional Neural Networks (CNN), VGG19, VGG16, ResNet50, EfficientNet, and vision Transformer, were trained on a breast cancer ultrasound dataset of mammograms. Features such as texture, density, and shape were extracted from each image for analysis. The different models achieved high accuracies of 9 9 % 9 1 %, 8 0 %, 9 9 %, 9 5 %, and 8 9 %, respectively. LRP is then used to explain the model's classification by highlighting image regions most relevant to its decision. This approach aims to attain high diagnosis accuracy while also offering interpretable insights into the model's logic, potentially increasing its reliability in clinica contexts. By automating the interpretation of mammographic images, this has the potential to improve diagnosis accuracy and patient outcomes. © 2024 IEEE.

关键词： machine vision

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Research on the Application of machine vision Technology in Industrial Automation Assembly Line

Research on the Application of Machine Vision Technology in ...

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2024 International conference on Mechatronics and Intelligent Control, ICMIC 2024

作者： Yang, Rongqiang Jiang, Quanxinxin Intelligent Manufacturing College Jingchu University of Technology Jingmen448000 China

ISBN: (数字)9781510686847

ISBN: (纸本)9781510686830

With the rapid development of industrial automation, the application of machine vision technology in assembly lines is becoming more and more extensive. This study mainly discusses the application of machine vision technology in industrial automation assembly lines, focusing on the visual inspection system based on an edge detection algorithm. By using the edge detection algorithm in image processing technology, the accuracy of the workpiece's position, shape and size is realized, effectively improving the automation level and work efficiency of the assembly line. This paper designs a complete machine vision system, including image acquisition, preprocessing, feature extraction and subsequent detection algorithm. This paper conducts detailed algorithm simulation and model testing to verify the system's effectiveness. The simulation results show that the designed edge detection algorithm has high accuracy and stability in the industrial automation environment;the detection accuracy reaches 0.01mm, and the system error is controlled within 0.5%. This study provides strong technical support for the future intelligent development of industrial automation assembly lines. © 2025 SPIE.

关键词： machine vision

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Edge deployed satellite image classification with TinEViT a X-Cube-AI compatible efficient vision transformer

Edge deployed satellite image classification with TinEViT a ...

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conference on Real-Time image processing and Deep 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)9781510673878;9781510673861

Lower resolutions and a lack of distinguishing features in large satellite imagery datasets make identification tasks challenging for traditional image classification models. vision Transformers (ViT) address these issues by creating deeper spatial relationships between image features. Self attention mechanisms are applied to better understand not only what features correspond to which classification profile, but how the features correspond to each other within each separate category. These models, integral to computer vision machine learning systems, depend on extensive datasets and rigorous training to develop highly accurate yet computationally demanding systems. Deploying such models in the field can present significant challenges on resource constrained devices. This paper introduces a novel approach to address these constraints by optimizing an efficient vision Transformer (TinEVit) for real-time satellite image classification that is compatible with ST Microelectronics AI integration tool, X-Cube-AI.

关键词： ViT STM-32 X-Cube-AI Satellite image Classification Efficient AI EuroSAT

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image Enhancement for Iris Analysis Using Deep Learning Algorithms - A Brief Survey 2

Image Enhancement for Iris Analysis Using Deep Learning Algo...

引用

2nd International conference on Emerging Trends in Information Technology and Engineering, ic-ETITE 2024

作者： Poovayar Priya, M. Ezhilarasan, M. Pondicherry Engineering College Dept. of Computer Science and Engineering Puducherry India Pondicherry Engineering College Dept. of Information Technology Puducherry India

ISBN: (纸本)9798350328202

With applications including medical image analysis, picture sharpening and restoration, robot vision, pattern recognition, and video processing, among many others, image enhancement is the main topic in image processing and computer vision. There have been many algorithms for improving images established in the past. More extensive work on image-enhancing techniques has been done utilizing deep learning models in recent years, as these algorithms have achieved their peak in vision applications. In this study, we present a thorough analysis of machine learning, deep learning, and literature. The literature on image enhancement is then summarised, and future approaches for iris analysis are discussed. © 2024 IEEE.

关键词： image enhancement

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3D Coordinate Measurement using an Optical-Probe Single-Camera vision System with an Initial Measuring Net 2

3D Coordinate Measurement using an Optical-Probe Single-Came...

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

作者： Wang, Wei Guo, Lei Sun, Bo Liu, Haiqing School of Precision Instrument and Opto-electronics Engineering Tianjin University Tianjin China Technology Co. Ltd Hangzhou China

ISBN: (纸本)9798350390254

The single-camera vision measurement system based on an optical probe (a light pen) has been developed quickly and used widely in the field of 3D coordinate measurement because of its simplicity of operation, low costs, and portability. However, as the measuring space enlarges and requirements of measuring uncertainty grow, the limitations of the optical-probe single-camera 3D coordinate vision measurement system prevent it from satisfying requirements. Thus, an optical-probe single-camera vision method based on an initial net is proposed in this paper. We incorporate bundle adjustment, which is largely used in movable vision measurement. Using the proposed method, we applied a fixed-size optical probe to realize small- and large-scale measurements, maintaining the same accuracy. Experiments verified the feasibility of the proposed method. © 2024 IEEE.

关键词： Probes

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Design Mechanism of Intelligent Fatigue Driving Behavior Detection System Based on machine vision 7

Design Mechanism of Intelligent Fatigue Driving Behavior Det...

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7th IEEE Eurasian conference on Educational Innovation, ECEI 2024

作者： Yu, Qiao Fuzhou Melbourne Polytechnic Fuzhou China

ISBN: (纸本)9798350307207

An intelligent fatigue-driving detection system was developed utilizing machine vision. In the system, image acquisition, processing, and fatigue feature extraction were integrated to enhance precision and operational efficiency. In image acquisition and processing, it is important to configure strategically in-car cameras and employ sophisticated techniques such as image denoising and enhancement. In the fatigue feature extraction, expression recognition algorithms were used for precise calibration and tracking facial areas, contributing to nuanced driver analysis. In the construction of the fatigue-driving detection system, the platform and tools were developed with a detailed exposition. This system solves the limitations of traditional fatigue detection systems and provides a refined and accurate solution applicable to real-world scenarios. The amalgamation of these elements underscores a holistic and forward-thinking approach to solving challenges associated with fatigue-related incidents in driving scenarios. © 2024 IEEE.

关键词： Computer vision

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