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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems VI"

共 15618 条记录，以下是731-740 订阅

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Improved Surface Defect Classification from a Simple Convolutional Neural Network by image Preprocessing and Data Augmentation 1

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10th International Work-conference on the Interplay Between Natural and Artificial Computation (IWINAC)

作者： Lopez de la Rosa, Francisco Moreno-Salvador, Lucia Gomez-Sirvent, Jose L. Morales, Rafael Sanchez-Reolid, Roberto Fernandez-Caballero, Antonio Univ Castilla La Mancha Inst Invest Informat Albacete Calle Invest 2 Albacete Spain Univ Castilla La Mancha Dept Ingn Elect Elect Automat & Comunicac Ave Espana S-N Albacete Spain Univ Castilla La Mancha Dept Sistemas Informat Ave Espana S-N Albacete Spain

ISBN: (数字)9783031611377

ISBN: (纸本)9783031611360;9783031611377

Convolutional neural networks (CNNs) play an important role in an increasing number of image processing tasks. There is an obvious demand to improve their classification performance and efficiency. Current research in this area tends to focus on developing increasingly complex models and algorithms to achieve this end. However, research into computer vision techniques and data augmentation tends to be neglected. This paper demonstrates that even a very simple CNN model achieves high performance in surface defect classification on the NEU dataset thanks to image preprocessing and data augmentation. The initial F1-score of 0.9646 without image preprocessing increases to 0.9727 when preprocessing is carried out. The simple CNN then achieves an F1-score of 0.9854 after data augmentation.

关键词： Surface defect classification image preprocessing data augmentation convolutional neural network

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Helmet Detection Using YOLO-v5 and Paddle OCR for Embedded systems 4th

Helmet Detection Using YOLO-v5 and Paddle OCR for Embedded...

引用

4th International conference on Recent Trends in Machine Learning, IoT, Smart Cities, and Applications, ICMISC 2023

作者： Kulkarni, Uday Chikkamath, Satish Kasigavi, Apoorva K. Venkatesh, Smitha K. Venkatesh, Siri K. Bhat, Sindhu Meena, S.M. KLE Technological University Karnataka Hubli India

ISBN: (纸本)9789819994410

One of the best methods to lessen the chance of suffering brain injuries in a two-wheeler collision is to wear a helmet, but many riders choose not to do so for a variety of reasons, including comfort, convenience, or ignorance. By automatically detecting and identifying riders who are not wearing helmets, automatic helmet detection tools can assist in overcoming these difficulties. These tools may result in fines or other consequences that may serve as an incentive for riders to wear helmets. Thousands of images and video frames can be quickly scanned and analyzed using powerful image processing algorithms employed in automatic detection systems, yielding consistent, accurate results without the possibility of human mistake. The YOLO-v5 object detection technique utilizes deep learning to detect and identify objects in a frame of an image or video. With the help of this model riders without helmet can be detected, and their license plate can be extracted. Optical character recognition (OCR) technique called Paddle OCR can precisely extract and identify text from an image or video frame. With the aid of OCR, the license plates can be recognized and can be stored. The proposed tool accurately detects the license plate of riders without helmets by bringing together these two potent models. Both the YOLO-v5 model and Paddle OCR model were trained using custom datasets with 1000 images each and obtained accuracy by 91%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep neural networks

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Research and application of active monitoring technology for power transmission and transformation construction safety in complex environments 9

Research and application of active monitoring technology for...

引用

9th International conference on Energy System, Electricity, and Power, ESEP 2024

作者： Lin, Zhikan Li, Wenxin Jin, Di Liu, Yuzheng Zhou, Guosheng Chen, Wei Lin, Yujie Lin, Cheng Wu, Jinshan Lin, Junjie Qiu, Peng State Grid Fujian Electric Power Co. Ltd. Construction Branch Fujian Building A No.9 Qinyuan Branch Road Jin'an District Fujian Province Fuzhou City China

ISBN: (纸本)9781510691728

Monitoring systems at power transmission and transformation construction sites often face challenges from complex environments such as haze, low lighting, and strong winds, leading to degraded video quality and stability. To enhance the reliability of these monitoring systems, this study employs three algorithms: image dehazing, low-light enhancement, and wind-induced blur removal. These algorithms are integrated through adaptive weight adjustment and parallel processing. This integration strategy enables the monitoring system to select the optimal algorithm in real-time based on environmental conditions and maintain stable image quality under multiple adverse factors. Experimental results show that the image dehazing algorithm improves PSNR to 28.37 dB in hazy environments, the low-light enhancement algorithm achieves a PSNR of 25.34 dB at night, and the wind-induced blur removal algorithm maintains an SSIM of 0.903 at a wind speed of 4 m/s. Transmission tests in weak signal areas indicate that a compression ratio of 30: 1 to 40: 1 can effectively balance video quality and bandwidth requirements. The monitoring system that integrates multiple algorithms demonstrates good adaptability and robustness in complex environments, providing reliable technical support for safety monitoring at power transmission and transformation construction sites. © 2025 SPIE.

关键词： image compression

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Comparison of DAT with DCT in a viewpoint of Current image processing and Analysis Trends 12

Comparison of DAT with DCT in a Viewpoint of Current Image P...

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12th IEEE International Scientific Technical conference on Dependable systems, Services and Technologies (DESSERT) - Dependable and Trustworthy AI, IoE, Big Data Analytics for a Safe and Secure World

作者： Makarichev, viktor Lukin, Vladimir Brysina, Iryna Natl Aerosp Univ KhAI Dept Comp Syst Networks & Cybersecur Kharkiv Ukraine Natl Aerosp Univ KhAI Dept Informat Commun Technol Kharkiv Ukraine Natl Aerosp Univ KhAI Dept Higher Math & Syst Anal Kharkiv Ukraine

ISBN: (纸本)9798350333046

Digital images are a type of data that has many applications. There are many constructive tools for their analysis and processing. In particular, various discrete transforms are used in order to get useful data features. Here, discrete atomic transform (DAT) and discrete cosine transform (DCT), which are discrete data transforms based, respectively, on atomic and trigonometric functions, are compared in a viewpoint of current image processing and analysis trends. Nowadays, due to a combination of challenges, it is of particular importance to develop such algorithms that provide data compression and protection features in combination with artificial intelligence oriented format. For different reasons, in particular functional properties, application of such non-classic tools as atomic functions to solving this problem seems to be promising. Trigonometric functions are widely used in image processing and can be de-facto considered as a standard. In this research, we provide a comprehensive comparison of DAT and DCT using different criteria, as well as discuss their strengths and weaknesses in the context of the problem considered.

关键词： image processing image representation discrete atomic transform discrete atomic compression discrete cosine transform

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Medical image Segmentation Techniques: Advances and Challenges

Medical Image Segmentation Techniques: Advances and Challeng...

引用

International image processing, Applications and systems conference (IPAS)

作者： Hela Boulehmi Rania Linda Filali ISET de Bizerte DGET Bizerta Tunisia

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

Medical image segmentation plays a pivotal role in computer-aided diagnosis by facilitating the extraction of essential features necessary for disease detection and treatment strategies. The continuous progress in image processing technologies has led to the development of numerous segmentation methods, encompassing traditional algorithms, machine learning (ML)-driven approaches, and cutting-edge deep learning (DL) techniques. This study undertakes a comparative evaluation of these methods, focusing on their efficiency, accuracy, and suitability across different medical imaging modalities. It also delves into prominent segmentation techniques like thresholding, region-based methods, edge detection, graph cuts, active contour models, and convolutional neural networks (CNNs). Additionally, the paper explores ongoing challenges and prospective advancements aimed at enhancing segmentation efficacy in medical imaging.

关键词： Deep learning image segmentation Thresholding (Imaging) Machine learning algorithms image edge detection Focusing Feature extraction Convolutional neural networks Medical diagnostic imaging Active contours

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Research on Automatic Screening Algorithm of Electric Power image Recognition Data Based on Artificial Intelligence 2

Research on Automatic Screening Algorithm of Electric Power ...

引用

2nd IEEE International conference on Power, Electronics and Computer Applications, ICPECA 2022

作者： Cao, Gang Liu, Yaohong Chen, Liang Xu, Tong Yu, Ting Yang, Sixu Zhang, Yu Hu, Yi Meishan Power Supply Company of State Grid Sichuan Electric Power Company Sichuan Meishan620000 China

ISBN: (纸本)9781665442763

In this paper, artificial intelligence digital image processing technology is used to process power images to form an automatic power image screening system. This method can replace the traditional artificial power image recognition method. This paper studies the specific methods of power image processing and the feasibility of applying them to power image recognition systems. It studies various algorithms for image recognition data automatic screening, feature extraction and pattern recognition, and studies feature extraction for power images. As well as a variety of algorithms for model recognition, we have studied the monitoring and recognition algorithms for power images based on SIFT and RANSAC methods. The selected template image and the key points of the test image are roughly matched. RANSAC removes the wrong matching points. Affine transformation, positioning the set template image in the test image. © 2022 IEEE.

关键词： image recognition

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Optimization of Micro-Object Identification by Correcting Distorted image Points

Optimization of Micro-Object Identification by Correcting Di...

引用

2023 International Russian Smart Industry conference, SmartIndustryCon 2023

作者： Jumanov, Isroil I. Safarov, Rustam A. Djumanov, Olimjon I. Samarkand State University Intelligent Systems and Computer Technologies Samarkand Uzbekistan

ISBN: (纸本)9781665464291

A methodology for optimizing the identification, recognition and classification of micro-objects has been implemented using dynamic models for transforming the original image, synthesizing mechanisms for extracting redundant information structures and using histological, morphological, and texture characteristics of images. Implemented mechanisms for detecting, highlighting and correcting the values of distorted points by embedding new data, segmenting the contour, reducing redundant fragments, and setting variables. Identification algorithms have been developed that take into account the correlations of a set of points, the dynamics of their change, matrices of coordinates of points, deformation of segments and the selection of homogeneous areas of images. A comparative analysis of the effectiveness of the mechanisms for pre-processing images, recognition and classification based on testing pictures of medical diagnostics, and pollen grains in solving problems of selection and seed production of grain was carried out. Modified component schemes, and learning algorithms for a three-layer neural network. A generalized mechanism of identification is implemented in case of non-linearity of connections "inputs and outputs"under conditions of a priori insufficiency and uncertainty. Five types of neural network software modules were tested with supervised and unsupervised learning algorithms based on network learning with modified samples with vector quantization, clustering, and identification with "sliding windows". The efficiency of identification with filtrations in the presence of "noise", approximation of the reference points of the curve of the contour of images of micro-objects has been studied. © 2023 IEEE.

关键词： Dynamic models

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Photoelectric Intelligent Detection Platform Based on Multifunctional Edge Computing Equipment 43

Photoelectric Intelligent Detection Platform Based on Multif...

引用

43rd Chinese Control conference, CCC 2024

作者： Xu, Qianjing Chen, Wenjie Xue, Zhijun State Key Lab of Intelligent Control and Decision of Complex Systems Beijing10081 China Beijing Institute of Technology School of Automation Beijing100081 China

ISBN: (纸本)9789887581581

In order to study to design a photoelectric intelligent platform based on multifunctional edge computing equipment by using photoelectric detection equipment TC505C, RK-Series Development Kits computing platform and Nvidia Jetson Series. The photoelectric intelligent platform can be used to run multiple target detection algorithms after design and implementation. Completed as follows. First of all, after the first stage of the photoelectric intelligent platform is completed, the host computer can establish communication with the photoelectric detection equipment, carry out automatic or manual servo control on the photoelectric detection equipment, switch its visible and infrared field of view and view it in real time through the monitor, and realize simple tracking function through the image processing box. Secondly, after the second stage of the photoelectric intelligent platform is built, under the premise of ensuring the normal availability of servo function, a variety of target detection algorithms can be debugged on multifunctional edge computing equipment. Overall, this paper uses the modular design concept to design, debug and implement the system in stages. Pay attention to the synchronous debugging of hardware and software functions of each module to ensure the stability of each module. Finally completed the design goals of each stage. © 2024 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Program debugging

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Leveraging Content Based image Retrieval Using Data Mining for Efficient image Exploration 1

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4th International conference on Mobile Radio Communications and 5G Networks, MRCN 2023

作者： Kaur, Jaspreet Gupta, Divya Singh, Amrinder Shah, Syed Hassan Ahmed Department of Computer Science and Engineering Chandigarh University Mohali 140413 India University Institute of Teachers Training and Research ChandigarhUniversity Punjab Mohali India California State University FullertonCA United States

ISBN: (数字)9789819707003

ISBN: (纸本)9789819706990

A content-based image Retrieval (CBIR) has become an essential tool for managing and searching large-scale images. However, the accuracy and performance of CBIR systems can be improved by combining data mining techniques. Content-based retrieval (CBR) uses the properties and characteristics of the content itself to search for and retrieve information from a big database instead of depending on text or metadata. CBR is very helpful in research, where it is necessary to swiftly and effectively examine vast amounts of data. According to the results, data mining techniques can considerably increase the retrieval process accuracy and effectiveness. Similar photos can be grouped together using clustering algorithms, common patterns of visual features can be found using association rule mining, and images can be classified using classification techniques. In order to create a system for content-based image retrieval and processing, we studied the retrieval of images from huge databases using a variety of feature extraction and matching techniques. The demand for CBIR development came as a result of the sharp rise in image database volumes and their widespread use in several applications. The description of basic feature extraction methods including texture, color, and form is provided in this study. Once these features are retrieved and then used for comparing photos based on similarity. This study suggests a cutting-edge system design for CBIR system that integrates content-based picture and color analysis with data mining methods. This work is intended to develop a segmentation module for the CBIR system. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Feature extraction

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International conference on Artificial Intelligence, Computer, Data Sciences, and Applications, ACDSA 2024

International Conference on Artificial Intelligence, Compute...

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2024 International conference on Artificial Intelligence, Computer, Data Sciences, and Applications, ACDSA 2024

ISBN: (纸本)9798350394528

The proceedings contain 158 papers. The topics discussed include: duct inspection and monitoring robot;deep learning-based approaches for preventing and predicting wild animals disappearance: a review;classification and tracking of items on a moving conveyor belt using convolutional networks and image processing;critical analysis of the 220/110/20 kV Sardanesti power substation from Romania in the context of identification elements of instability and insecurity;machine learning based collaborative prediction of SSD failures in the cloud;the impact of explainable ai on low-accuracy models: a practical approach with movie genre prediction;utilizing transfer learning-based algorithms for breast ultrasound data in multi-instance classification;predictive maintenance model-based on multi-stage neural network systems for wind turbines;and using teaching learning-based optimization with convolutional neural network to detect pneumonia based on chest X-Ray images.

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