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检索条件"任意字段=4th International Conference on Computer Vision, Pattern Recognition and Image Processing"

共 5158 条记录，以下是11-20 订阅

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Dynamic Color Identification Using computer vision: OpenCV with Pandas 4

Dynamic Color Identification Using Computer Vision: OpenCV w...

引用

4th international conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Mohanraj, E. Kirubashini, V. Gobika, B. Archana, A.M. K.S. Rangasamy College of Technology Department of Artificial Intelligence and Data Science Tamil Nadu India

ISBN: (纸本)9798331523923

computer vision, the cornerstone of modern artificial intelligence. Moving forward with the development of new tools and techniques, OpenCV (Open Source computer vision Library) coupled with pandas' powerful data management capabilities has emerged as a versatile framework for real-world image processing tasks. this paper explores the application of OpenCV and pandas for real-time color recognition and OpenCV's enhanced functionality, such as video stream processing taking advantage of Pandas with efficient data management. the proposed system captures real-time images and processes user-defined interactions to identify the color. By combining pandas, the system optimizes access to the predefined color data stored in a CSV file, allowing for seamless color matching. this OpenCV and pandas together enables the system to dynamically display color names, RGB components and hex values in real time enhancing user interaction by double clicking. the combination of these technologies represents a powerful method for solving image-based problems for innovation and efficiency. Real world applications demonstrate their capabilities. © 2025 IEEE.

关键词： Information management

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Research on traffic sign recognition algorithms for autonomous vehicles in smart city traffic systems 4

Research on traffic sign recognition algorithms for autonomo...

引用

4th international conference on computer vision and pattern Analysis, ICCPA 2024

作者： Wang, Chuanfei School of Government Beijing Normal University Beijing100875 China

ISBN: (纸本)9781510682528

With the continuous advancement of smart city construction, autonomous driving technology is playing an increasingly important role in urban traffic systems. this study aims to explore the development and optimization of traffic sign recognition algorithms for autonomous vehicles in smart city traffic environments. By comprehensively analyzing current traffic sign recognition technologies, this paper proposes a traffic sign recognition system based on the YOLOv5 algorithm and utilizes the open-source COCO dataset for model training and testing. the images were preprocessed and annotated, employing CSPDarknet53 as the backbone network, which effectively extracts image features through multiple convolutional layers and residual blocks. A deep learning model was trained, capable of recognizing and classifying various traffic signs such as stop, speed limit, and turn signs. the research results indicate that the model demonstrates high average precision (AP) on the COCO dataset, effectively identifying traffic signs of different sizes and angles, even in complex backgrounds, with high robustness. Compared to traditional methods, the recognition accuracy has improved by 15%, and it has a significant advantage in real-time processing. © 2024 SPIE.

关键词： Object detection Detection and tracking algorithms Unmanned vehicles Performance modeling Feature extraction image processing Data modeling image segmentation Mathematical optimization

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Cow face recognition based on Transformer group 4

Cow face recognition based on Transformer group

引用

4th international conference on computer vision and pattern Analysis, ICCPA 2024

作者： Li, Xujian Liu, Yunhao College of Computer Science and Engineering Shandong University of Science and Technology Shandong China

ISBN: (纸本)9781510682528

this paper found that in existing cattle face recognition algorithms, convolutional neural networks (CNN) are more commonly used. the processing speed and performance of CNNs in processing large data are often limited by the receptive field and the number of network layers. At the same time, the visual transformer has achieved great success due to its high performance and excellent ability to process large data. therefore, this paper borrows the traditional cattle face recognition process, using the DETR algorithm for object detection and the Swin-transformer algorithm for image classification, thereby improving the running efficiency and enhancing the ability to process large data. Comprehensive experiments show that the algorithm in this paper is superior to existing traditional models, achieving a good balance between speed and accuracy. Compared with traditional algorithms, this algorithm utilizes the emerging Transformer group, and not only can effectively identify the unique identity of cattle's front face, but also has good effect on identifying the unique identity of cattle's side face and rear face due to using the Transformer group to train and test a large amount of data. © 2024 SPIE.

关键词： Convolutional neural networks

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Visual Interface Design and Application of image processing System 4

Visual Interface Design and Application of Image Processing ...

引用

4th international conference on Electronic Information Engineering and computer Science, EIECS 2024

作者： Sun, Xiaoxue Chen, Chen Gao, Guoren Shi, Qilu Jilin Agriculture Science and Technology University Jilin Mechanical Engineering Department Jilin China State Grid Liaoning Extra High Voltage Company Liaoning Anshan China

ISBN: (纸本)9798331531409

With the rapid development of computer technology, image processing technology, as an important branch in the field of computer vision, has been widely researched and applied. In order to facilitate users to perform image processing and analysis, an image processing system is designed based on Matlab-GUI. this system integrates functional modules such as color/brightness adjustment, basic functions, spatial filtering and denoising, image enhancement/restoration, adding noise, and edge detection. Combined with RGB waveform diagrams and RGB component waveform diagrams, it realizes visual analysis of image color distribution and tonal hierarchy. the system has a friendly interface and is easy to operate. It can provide strong support for tasks such as image recognition and promote the continuous innovation and development of image processing technology. © 2024 IEEE.

关键词： MATLAB

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Underwater SLAM system based on image feature point information enhancement 4

Underwater SLAM system based on image feature point informat...

引用

4th international conference on computer vision and pattern Analysis, ICCPA 2024

作者： Tang, Yijie Wang, Jin College of Automation Jiangsu University of Science and Technology Jiangsu Zhenjiang China

ISBN: (纸本)9781510682528

In this paper, we propose an underwater synchronous localization and mapping algorithm based on image information enhancement, which can reduce the problems of image blurring and contrast reduction caused by environmental factors by processing underwater images, and improve the performance of visual odometer in underwater environment. On the basis of image processing, the acoustic sensor with less influence on underwater environmental factors is introduced to help the system to carry out target scale recovery and absolute distance detection, strengthen the tracking and matching of feature points, and improve the accuracy of state estimation. © 2024 SPIE.

关键词： Underwater imaging

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Research on Machine vision Intelligent Detection System Based on Faster R-CNN 4

Research on Machine Vision Intelligent Detection System Base...

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4th IEEE international conference on Data Science and computer Application, ICDSCA 2024

作者： Wang, Xueting Zeng, Fanshuo Song, Weizhao Zhang, Zhipeng He, Huitong Zhou, Zheyuan Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (纸本)9798350368239

With the rapid development of artificial intelligence technology, visual inspection and image processing algorithms have been continuously improved in accuracy and efficiency, and intelligent inspection systems based on machine vision have been widely used in product quality control, defect detection and process monitoring. In this paper, a machine vision intelligent inspection system based on Faster Region-based Convolutional Network (Faster R-CNN) is proposed, which aims to improve the detection accuracy and efficiency. In this paper, the overall architecture of the system is introduced, including the image acquisition module, the data preprocessing module and the detection module based on Faster R-CNN. In the data preprocessing module, the Deep Convolution Generative Adversarial Networks (DCGAN) algorithm was proposed for image expansion generation, and high-resolution image samples that were basically consistent with the collected image datasets were obtained. then, the Faster R-CNN model was optimized and adjusted for specific application scenarios, so that the average accuracy (mAP) of the model reached 78.23% and the FPS value reached 11.0. ©2024 IEEE.

关键词： Faster R-CNN image processing image recognition Machine vision intelligent inspection system

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An Efficient ODR for computer Night vision Using Multi-Scale Retinex Network-Aided Enhanced images 4

An Efficient ODR for Computer Night Vision Using Multi-Scale...

引用

4th international conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Charles Prabu, V. Pandiaraja, P. Sathiyamoorthi, V. Durgadevi, P. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai India Government Polytechnic College Department of Computer Engineering Baisuhalli Tamilnadu Dharmapuri India Srm Institute of Science and Technology Department of Computer Science and Engineering Vadapalani Campus Tamilnadu Chennai India

ISBN: (纸本)9798331508845

In the current scenario, recognizing various objects and tracking their movements in the real-time surveillance footage is the most difficult task. To detect objects, a combination of image processing and computer vision algorithms is utilized. computer-vision based automatic human activity recognition from surveillance video can be utilized for applications such as the identification of violent acts and the study of human behavior. Consequently, this work implements a new "Object Detection and recognition (ODR)"model for computer night vision utilizing a deep learning technique. In order to improve the supplied input image, the combined images are first transmitted to the "Multi-scale Retinex (MSR)"model. the You Only Look Once Version 7 (YoloV7) model uses the improved image from MSR as input to identify and recognize things. After conducting experiments, the implemented ODR model on the ExDark Dataset obtained an efficiency of 94.8%. © 2024 IEEE.

关键词： Deep learning

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Comprehensive study of image processing techniques for low-altitude target recognition 4

Comprehensive study of image processing techniques for low-a...

引用

4th international conference on computer vision, image and Deep Learning, CVIDL 2023

作者： Shao, Jianming Yu, Ya Xu, Zheng Ye, Chen Yan, Minghao Tong, Yuhao State Grid Wuxi Power Supply Company Wuxi China Jiayuan Technology Co. Ltd Nanjing China

ISBN: (纸本)9798350326444

this article presents a comprehensive overview of image processing techniques for low-altitude target recognition. the analysis includes various algorithms that incorporate shape, texture, and motion features, highlighting their advantages, disadvantages, and applicability. Additionally, this article explores multiple methods for low-altitude target recognition based on multi-feature fusion, comparing their advantages and disadvantages, and discussing future technological development directions. the article serves as a valuable reference for researchers interested in low-altitude target recognition, providing insights on algorithm selection and the application of multi-feature fusion. the presented research is expected to contribute to the advancement of low-altitude target recognition technology. © 2023 IEEE.

关键词： image processing

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computer graphics and image processing techniques based on visual communication design 4

Computer graphics and image processing techniques based on v...

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4th international conference on computer vision, Application, and Algorithm, CVAA 2024

作者： Chen, Zhaoguo College of Arts Shandong Agricultural Engineering University Shandong Province Jinan250103 China

ISBN: (数字)9781510687622

ISBN: (纸本)9781510687615

To fully harness the capabilities of computer graphics and image processing technologies and elevate the quality of visual communication design, this paper presents a comprehensive suite of innovative methodologies. Firstly, the fundamental principles of primary colors are utilized to formulate a series of vector functions, facilitating the precise calculation of error distances and the establishment of an intricate visual communication zoning model. Subsequently, through the exhaustive extraction of pertinent feature information from graphics and images, we achieve the scientific encoding of coefficient-constrained features and determine the corresponding hyperplanes to acquire the relevant parameters for visual communication. Eventually, the implementation of advanced design image expression algorithms markedly enhances the effectiveness of visual communication image representation. the empirical findings reveal that these visual communication design methods exhibit remarkable reliability and feasibility, effectively boosting image recognition accuracy across four distinct image categories with varying levels of complexity, thereby yielding exceptional visual communication outcomes. © 2025 SPIE.

关键词： Design Visual communications Visualization computer graphics image processing image storage Color image segmentation image enhancement

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Fourth international conference on computer vision, Application, and Algorithm, CVAA 2024

Fourth International Conference on Computer Vision, Applicat...

引用

4th international conference on computer vision, Application, and Algorithm, CVAA 2024

ISBN: (纸本)9781510687615

the proceedings contain 122 papers. the topics discussed include: DFrFT-ES model for emotion recognition based on fractional Fourier transform of EEG signals;research on traffic sign recognition under complex meteorological conditions;diffusion-augmented learning for long-tail recognition;apple leaf scab recognition using CNN and transfer learning;container image management in cloud-edge environments: an image deletion method based on layer affinity;computer graphics and image processing techniques based on visual communication design;dynamic fusion and non-negative matrix factorization-based multi-view clustering method;convolutional recurrent neural network-based EEG signal classification in motor imagery;and sentiment classification of MOOC courses by merging local context focus and bi-directional gated recurrent unit.

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