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

共 16443 条记录，以下是561-570 订阅

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An image processing Based Approach Towards Recognition of Underground Cable Defects 2

An Image Processing Based Approach Towards Recognition of Un...

引用

2nd International conference on Smart Technologies for Power and Renewable Energy, SPECon 2024

作者： Thampi, Aparna Mahendran, N. Abraham, Linu Mariam Sidhartha, M.S. Apj Abdul Kalam Technological University Department of Electrical and Electronics Engineering Saintgits College of Engineering Kottayam Kerala India Tata Power Delhi Distribution Ltd India Digital Core Technology Kochi India

ISBN: (纸本)9798350383041

This paper presents a novel approach for detecting possible faults in underground cables at the manufacturing industry using image processing techniques. With the increasing adoption of underground cables to minimize faults and outages in power transmission systems, ensuring the quality of these cables becomes crucial. Detecting defects at the manufacturing stage can save significant costs and effort by identifying faulty cables before they are deployed in the field. We propose a system that utilizes image processing algorithms to analyze cable images and identify potential faults, such as unbalanced currents, cable sheath faults, and short circuits. The system also calculates the area of each core to ensure equal current flow, and estimates the total cross-sectional area of the cable to assess the adequacy of insulation. The proposed approach offers a non-destructive and efficient method to enhance quality control in the manufacturing process of underground cables. © 2024 IEEE.

关键词： Underground cables

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A Fast image Matching Approach in Fatigue Testing of Wall Plate Structures

A Fast Image Matching Approach in Fatigue Testing of Wall Pl...

引用

IEEE International conference on Information Communication and Signal processing (ICICSP)

作者： Zhang Wendong Shi Baoquan Chen xianmin Lv Yuanbo National Key Laboratory of Strength and Structural Integrity AVIC Aircraft Strength Research Institute Xi'an China School of Mechano-Electronic Engineering Xidian University Xi'an China

ISBN: (数字)9798350355895

ISBN: (纸本)9798350355901

To address the challenges in motion vector estimation due to local non-uniform deformation of specimens during fatigue tests, this paper proposes an image matching method that combines template sub-image techniques with a Kalman filter. This method employs a mesh motion estimation model, initially using the Kalman filter to predict the matching point positions of mesh nodes. Subsequently, template matching is utilized to achieve precise matching of these nodes. The algorithm is accelerated through GPU-CPU heterogeneous parallel computing technology. Test results indicate that the GPU-accelerated image matching method attains the accuracy of the CPU-based algorithm while significantly reducing processing time, thereby fully meeting the real-time requirements of online image feature matching applications.

关键词： Accuracy Deformation image matching Signal processing algorithms Fatigue Prediction algorithms Vectors Real-time systems Kalman filters Testing

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Coding Improvement in the DAC Algorithm 13

Coding Improvement in the DAC Algorithm

引用

13th International conference on Dependable systems, Services and Technologies, DESSERT 2023

作者： Makarichev, Viktor Lukin, Vladimir Brysina, Iryna National Aerospace University KhAI Dept. of Information-Communication Technologies Kharkiv Ukraine National Aerospace University KhAI Dept. of Higher Mathematics and System Analysis Kharkiv Ukraine

ISBN: (纸本)9798350396119

Digital images are a particular type of data. They have a lot of applications. Huge image datasets have been collected. Their processing, storing, analyzing, and transferring via networks require great expenses. For this reason, a development of low resource intensive image processing algorithms is of particular importance. Discrete atomic compression (DAC) is an image compression algorithm based on atomic functions. It provides a combination of data compression and protection features with machine learning oriented data representation. Also, DAC has low time and spatial complexity, which makes its application promising. In this paper, we improve a compression performance of this algorithm. It is shown that, by applying the proposed coding scheme a compression ratio can be increased and, hence, greater memory savings can be obtained. © 2023 IEEE.

关键词： image enhancement

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Enhancing Surveillance systems with YOLO Algorithm for Real-Time Object Detection and Tracking 2

Enhancing Surveillance Systems with YOLO Algorithm for Real-...

引用

2nd International conference on Automation, Computing and Renewable systems, ICACRS 2023

作者： Anish, A. Sharan, R. Hema Malini, A. Archana, T. Department of ECE Saveetha Engineering College Chennai India

ISBN: (纸本)9798350340235

A Virtually Impaired Person (VIP) is unable to identify objects when they cannot recognize where the object is placed. The researchers are working on it to enhance object detection and help VIP. The challenges faced by researchers are performing detection under low-resolution images, insufficient sensors, portability, and cost. Making a compact device and alerting them is required. By considering the above-mentioned difficulties, an innovative solution is described in this research work. The growth of image processing and deep learning techniqueshas simplifiedthe complexity of processing data and provided accurate results within a limited time period. The suggested technique presented is a deep learning algorithm calledthe YOLO algorithm, which is combined with the web to predict objects accurately. For this approach, a dataset with a total of 500 images was chosen and trained. The proposed classifier result is satisfactory, and it achieved an overall accuracy of 94%. Furthermore, this proposed technique provides enough output in comparison with several other machine learning and image processing algorithms. © 2023 IEEE.

关键词： Deep Learning image processing Object Detection Visually Impaired Person (VIP) YOLO Algorithm

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A Comparative Study of VGG16, MobileNet, and Ensemble Methods on Fundus image-Based Cataract Detection

A Comparative Study of VGG16, MobileNet, and Ensemble Method...

引用

2025 International conference on Advancement in Data Science, E-learning and Information System, ICADEIS 2025

作者： Syahda, Tabina Shafa Nabila Ma'ady, Mochamad Nizar Palefi Lidiawaty, Berlian Rahmy Telkom University Surabaya Department of Information Systems Surabaya Indonesia

ISBN: (纸本)9798331513320

Artificial Intelligence (AI) in ophthalmology has been growing, driven by the increasing volume of clinical data that can be utilized for algorithm development. image processing on fundus disease is crucial in providing visual information for cataract detection. Therefore, the application of technology that can leverage fundus images for cataract detection is still a few. In this regard, several previous studies have explored CNN algorithms for automatic cataract screening using fundus images. However, other research groups have proposed or developed new algorithms as alternatives. This research compares two CNN models, VGG16 and MobileNet, while also incorporating additional approaches such as Ensemble Learning and Hybrid Method. VGG16 and MobileNet were chosen because they are known for their high accuracy performance and computational efficiency. Source data obtained from Kaggle as many as 1000 datasets. The results of the study indicate that the model with the Ensemble Learning approach achieved the highest average accuracy, followed by MobileNet, MobileNet + SVM, VGG16, and VGG16 + SVM, with accuracies of 0.9377;0.9150;0.9143;0.9129;and 0.9104, respectively. This research is expected to aid medical professionals in diagnosing cataracts more accurately and guide engineers in selecting the best machine-learning models for fundus image-based cataract detection systems. © 2025 IEEE.

关键词： Contrastive Learning

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Point Cloud Registration Algorithm Based on Neighborhood Feature Similarity for Contour Fitting 16

Point Cloud Registration Algorithm Based on Neighborhood Fea...

引用

16th International conference on Digital image processing, ICDIP 2024

作者： Yu, Yuewen Lin, Chuchang Chen, xiaobo xi, Juntong School of Mechanical Engineering Shanghai Jiao Tong University Shanghai200240 China Shanghai Key Laboratory of Advanced Manufacturing Environment Shanghai200030 China State Key Laboratory of Mechanical System and Vibration Shanghai200240 China

ISBN: (数字)9781510682917

ISBN: (纸本)9781510682900

This paper introduces a point cloud registration algorithm based on neighborhood feature similarity, specifically targeting the challenge of quality optimization in component assembly within the modern manufacturing industry, particularly for precise contour fitting. By enhancing the classic Iterative Closest Point (ICP) algorithm, the study focuses on feature extraction of contour point clouds and improving fitting accuracy. The algorithm incorporates neighborhood feature similarity based on curvature characteristics and multi-neighborhood normal vector variation, along with a loss function design based on contour deviation. Experiments on fitting actual headlight contour point clouds with CAD theoretical model point clouds demonstrate the algorithm's high precision and faster iteration speed in aligning narrow-band point clouds with unclear feature points, significantly outperforming traditional ICP and its variants. The research not only enhances component assembly quality but also provides an effective tool for contour point cloud fitting in complex manufacturing scenarios. © 2024 SPIE.

关键词： Point clouds Headlamps Detection and tracking algorithms Computer aided design Contour modeling Manufacturing Mathematical optimization Solid modeling Assembly equipment

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Low-complexity SSOR-based for the Space-time adaptive processing anti-jamming system 19

Low-complexity SSOR-based for the Space-time adaptive proces...

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19th International conference on Natural Computation, Fuzzy systems and Knowledge Discovery, ICNC-FSKD 2023

作者： Yu, Zhong Lu, Yi Cheng, Yifei Xu, Lilan Xi'an University of Posts & Telecommunications School of Communication and Information Engineering Xi'an China

ISBN: (纸本)9798350304398

The space-time adaptive algorithm greatly increases the computational complexity of the system due to the existence of a high-dimensional matrix inversion process in signal processing, and thus has many problems, such as poor real-time performance, high system resource consumption, and difficult engineering implementation. In this paper, based on the traditional space-time adaptive algorithm, we propose a high-dimensional matrix inversion iteration method based on symmetric successive over-relaxation, which dramatically reduces the computational effort and improves the convergence speed of the system. Analysis and simulation results show that, compared with the traditional space-time adaptive algorithm, the proposed algorithm achieves a reduction of the order of magnitude in computational complexity and has a broad application prospect in communication, radar, navigation and other interference environment time-varying scenarios. © 2023 IEEE.

关键词： Adaptive algorithms

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Enhancing image Denoising Performance through a Family of algorithms 2

Enhancing Image Denoising Performance through a Family of Al...

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2nd International conference on Paradigm Shifts in Communications Embedded systems, Machine Learning and Signal processing, PCEMS 2023

作者： Gupta, Omkar Kalapala, Irika Nikhita Bhurchandi, Kishor M. Visvesvaraya National Institute of Technology Department of Electronics and Communication Engineering Nagpur India

ISBN: (纸本)9798350310719

images are an integral and indispensable aspect of various disciplines, such as medicine, surveillance, and the entertainment industry. However, the quality of images can be severely compromised by the presence of sensor noise, quantization errors, or transmission errors. This research proposes a novel approach that combines wavelet thresholding and BM3D (Block-Matching and 3D Filtering) techniques for effective image *** efficacy of the methodologies is evaluated and compared to cutting-edge denoising techniques, demonstrating superior performance in both quantitative metrics and visual quality. Furthermore, the study delves into the intricate mechanisms underlying the denoising process and the impact of various parameters on the denoising performance, contributing significantly to the field of image denoising. © 2023 IEEE.

关键词： image denoising

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Real-time Dispersion Rendering Algorithm based on Caustics Map and Single Charge Coupled Device image Sensor 8

Real-time Dispersion Rendering Algorithm based on Caustics M...

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8th International conference on Intelligent Computing and Signal processing, ICSP 2023

作者： Luo, Yuanmeng Jiangnan University School of Artificial Intelligence and Computer Wuxi China

ISBN: (纸本)9798350302455

Dispersion effects are important in rendering scenes that contain translucent objects. Existing ray-tracing algorithms must rely on high-end GPU hardware to achieve real-time rendering when rendering dispersion. Based on the image-space caustic map technique, a concise and efficient real-time dispersion rendering method is proposed in the paper, in which the rendering efficiency is improved by reducing the number of monochromatic lights that need to be emitted based on the idea of single charge coupled device image sensor. A variable-size photon drawing strategy is proposed for filling the gaps formed by the insufficient number of monochromatic light samples. The experimental results show that the proposed method can achieve real-time rendering on PC. Our proposed method can simulate the whole continuous spectrum with 7 monochromatic lights of discrete sampled spectrum, reducing the computation and storage of rendering, and it improves the problem of noise formed by insufficient number of emitted rays. © 2023 IEEE.

关键词： Charge coupled devices

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Research on Collaborative Salient Object Detection Using Collaborative Feature Integration and Extraction 12

Research on Collaborative Salient Object Detection Using Col...

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12th International conference on Intelligent Control and Information processing, ICICIP 2024

作者： Chen, Yachun Lu, Mengqi Sun, Yu Zhang, Nina Zhou, Yanlong Zhang, Dehua College of Artificial Intelligence Henan University Zhengzhou China Miami College Henan University Zhengzhou China Comprehensive Information Support Center Shaanxi General Staff of Pap Xi'an China

ISBN: (纸本)9798350308020

Collaborative salient object detection is a critial preprocessing part of many complex computer vision tasks. Therefore, the study of co-salient object detection in complex scenes has important practical significance and value. This research presents a deep learning-based model that utilizes collaborative feature extraction and fusion. First, the salient object detection algorithm is designed to obtain single image salient feature maps(SISMs), combining VGG16 and normalized masking average pooling(NAMP) approach to fetch images. Then the correlation information is obtained by feeding the internal information and category features of a single image into the correlation fusion module. Additionally, to further enhance the performance, the self-correlation characteristics are rearranged. Finally, the proposed model and four advanced algorithms were tested on the public authoritative datasets. The result showed that our method had better performance in co-saliency detection, and the ablation study verified the effectiveness of each module. © 2024 IEEE.

关键词： Feature extraction

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