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

共 12948 条记录，以下是211-220 订阅

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Performance Evaluation of Underwater image Enhancement Techniques

Performance Evaluation of Underwater Image Enhancement Techn...

引用

2024 IEEE International conference on Information Technology, Electronics and Intelligent Communication systems, ICITEICS 2024

作者： Shubhangi, Adagale-Vairagar Gupta, Praveen CSMU Navi Mumbai Mumbai India

ISBN: (纸本)9798350382693

Absorption, scattering, and colour distortion make underwater photography difficult. Marine biology, underwater archaeology, and surveillance need better underwater photos. This research compares cutting-edge underwater image-enhancing methods to assess their efficacy and usefulness. This paper provides a comparative analysis of the various filtering approaches for underwater image enhancement, such as median filtering, average filtering, Gaussian filtering, Wiener filtering, and wavelet-based soft thresholding. The performance of these techniques is evaluated based on the mean square error, peak signal-to-noise ratio, average gradient, PCQI, and UICQI. The effectiveness of the suggested filtering scheme is validated on some sample images of the public UIEB dataset. It provides the analytical finding, advantages, disadvantages and performance of the different techniques for wide variety of underwater images enhancement. The paper also explores underwater image enhancement issues and future approaches. The promising research directions are integrating domain-specific information, real-time processing, and adaptive algorithms for dynamic underwater scenes. The discoveries help increase underwater photography visibility and interpretability, increasing underwater exploration and monitoring systems. © 2024 IEEE.

关键词： Underwater imaging

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2024 18th International conference on Open Source systems and Technologies, ICOSST 2024 - Proceedings

2024 18th International Conference on Open Source Systems an...

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18th International conference on Open Source systems and Technologies, ICOSST 2024

ISBN: (纸本)9798331508692

The proceedings contain 23 papers. The topics discussed include: solar powered auto irrigation monitoring system with plant health indication using image processing;large language model and artificial intelligence based human conversation agent;to enhance graph-based retrieval-augmented generation (RAG) with robust retrieval techniques;automatic timetable generation using neural networks trained by genetic algorithms;assessment of efficient and cost-effective vehicle detection in foggy weather;enhanced detection and prevention of SQL injection and cross-site scripting attacks in web applications: analyzing algorithms and threat modeling approaches;efficient duplicate question detection;and a comprehensive survey on computing services to detect stress and anxiety using smart devices.

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Cloud Architectural Design for image-based Vehicle Positioning in Traffic Management 28

Cloud Architectural Design for Image-based Vehicle Positioni...

引用

28th International conference on System Theory Control and Computing

作者： Beti, Iosif-Alin Herghelegiu, Paul-Corneliu Amarandei, Cristian-Mihai Caruntu, Constantin-Florin Gheorghe Asachi Tech Univ Iasi Dept Automat Control & Appl Informat Iasi Romania Gheorghe Asachi Tech Univ Iasi Dept Comp Sci & Engn Iasi Romania

ISBN: (纸本)9798350364309;9798350364293

Vehicle positioning algorithms are essential for improving traffic management and safety by accurately locating vehicles in real-time, and, thus, minimizing congestion and accidents. They also support the development of advanced driver assistance systems and autonomous vehicles, relying on precise positioning data for safe navigation. One of the solutions involves using image processing algorithms, which can have two approaches. One approach is decentralized, in which each vehicle performs its own computing steps and determines its position concerning the other nearby vehicles. The second approach, proposed in this paper, is centralized, where each vehicle sends data to a server that uses cloud computing to process all the data in real-time. As such, vehicles can create a more comprehensive view of the driving conditions in the area by using either of these two approaches, which can help them anticipate potential hazards and make more informed decisions.

关键词： computer vision traffic optimization vehicle platooning cloud computing 5G networks vehicle positioning V2V communication

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Optimized Machine Learning for Medical Data Analysis and Disease Prediction

Optimized Machine Learning for Medical Data Analysis and Dis...

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2024 International conference on Artificial Intelligence and Emerging Technology, Global AI Summit 2024

作者： Dubey, Anil Kumar Kapoor, Raman Saraswat, Mala Abes Engineering College Ghaziabad India Bennett University Greater Noida India

ISBN: (纸本)9798350379716

In the context of healthcare and human-computer interaction., this research study provides a thorough analysis of sophisticated computational algorithms for data classification, picture processing, and disease prediction. For multi-disease prediction and intrusion detection, various works have been investigated to arrive at optimal hybrid learning methods and biological feature selection techniques. The role of revolutionary blockchain-based data storage systems and enhanced analysis techniques of EEG data for brain illness detection have been reviewed. Furthermore, an improved bidirectional long short-term memory (LSTM) for time-series data categorization is also discussed. Some noteworthy studies such as the analysis of medical picture defects, application of immersive technology to diagnose Alzheimer's illness and improvements in image resolution and segmentation are also reviewed in this work. Based on the reviewed literature, an improved method is also proposed which is expected to assist with early diagnosis and risk reduction. © 2024 IEEE.

关键词： Brain-Computer Interface Feature Selection Techniques Hybrid Learning algorithms Intrusion Detection systems and Medical image Analysis

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ADIMPR-Net: Automatic Denoising Intra-pulse Multi-parameter Recognition with a Deep Multi-task Network

ADIMPR-Net: Automatic Denoising Intra-pulse Multi-parameter ...

引用

International conference on Electronic Engineering and Information systems (EEISS)

作者： Liu, Yingming Chen, Tao Harbin Engn Univ Coll Informat & Commun Engn Harbin Peoples R China

ISBN: (纸本)9798350351040;9798350351033

To concurrently address the challenges of automatic modulation recognition (AMR) and modulation parameter estimation (MPE) in radar signals, we introduce a multi-task learning (MTL) network architecture designed for intra-pulse multi-parameter recognition through automatic noise reduction. The proposed approach initiates by generating time -frequency image (rn) directly from noisy time-domain signal using a neural network. To effectively capture relevant features from TFls, we employ a VGG-like network Subsequently, the multitask learning framework is utilized to achieve AMR and MPE simultaneously. Four typical intra-pulse modulated signals were simulated in the experiments, simulation results verify the effectiveness and reliability of the proposed algorithms.

关键词： component: Radar signal Signal processing modulation recognition parameter estimation deep learning

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Algorithm for Changing the Size of a Multi-Element Aperture of a Two-dimensional Recursive-Separable Five-Stage Filter for Digital image processing 25

Algorithm for Changing the Size of a Multi-Element Aperture ...

引用

25th IEEE International conference of Young Professionals in Electron Devices and Materials, EDM 2024

作者： Kamenskiy, Andrey V. Kupriyanova, Kristina S. Podnebesnyy, Andrey V. Kuryachiy, Mikhail I. Borodina, Natalya Tatyana, Akaeva M. Dept. of Television and Control Tomsk State University of Control Systems and Radioelectronics Tomsk Russia

ISBN: (纸本)9798350389234

The paper presents a description of the developed algorithm for changing the size of a multi-element aperture of a recursive-separable five-stage filter for processing digital images generated by specialized optical systems. The developed algorithm has two features: it has the ability to change the size of a multi-element aperture during use;it makes it possible to use the recursive and separable properties. These capabilities make it possible to reduce the processing time of incoming data, which has a positive effect on the efficiency of the optical-electronic imaging system used. The paper presents a block diagram of a two-dimensional recursive-separable five-stage filter, with the help of which an algorithm was developed for changing the size of a multi-element aperture, and a study of the influence of the aperture size on the performance of the developed filter was carried out. As part of the performance evaluation, the developed algorithm was compared with the classical two-dimensional convolution algorithm. Developed algorithm was tested on three test images of different sizes and formats: 1300x700 Bitmap Picture, 1200x1200 Tagged image File, 2900x1900 Joint Photographic Experts Group. During the processing of test images, the dimensions of the multi-element aperture of the developed filter changed. As the aperture size increases, the performance evaluation results also increase. This shows that speeding up the algorithm is an actual task. However, the developed algorithm takes less time than the classical two-dimensional convolution algorithm, from which it can be concluded that the developed solution is effective for both conventional images and images from optoelectronic systems. © 2024 IEEE.

关键词： digital filters digital image processing optical-electronic systems performance real time recursively separable algorithms

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image processing Based on Inter-frame Color Feature Matching for Visible Light Positioning 2

Image Processing Based on Inter-frame Color Feature Matching...

引用

2nd International conference on Signal processing and Intelligent Computing, SPIC 2024

作者： Yu, Baozhu Liu, Xiangyu Chen, Yaoheng Guo, Lei Bao, Jianbo Sun, Jiandong Shenyang Ligong University School of Information Science and Engineering Shenyang China Southern University of Science and Technology Department of Computer Science and Engineering Shenzhen China Chongqing University of Posts and Telecommunications School of Communication and Information Engineering Chongqing China Nari Group Corporation State Grid Electric Power Research Institute Nanjing China

ISBN: (纸本)9798350368888

The indoor imaging visible light positioning (VLP) technology based on the image sensor (IS) utilizes existing indoor lighting infrastructure to provide high-precision indoor positioning services. However, due to the high complexity of traditional image processing algorithms, existing VLP systems are unable to provide real-time positioning services. In response to the above issue, we propose an image processing mechanism based on the inter-frame color feature matching method, which includes the equidistant sampling processing, low complexity decoding algorithm, and color feature matching-based LED region of interest (ROI) determination method. The results show that the system average positioning delay is 80.2ms, indicating better system real-time performance. © 2024 IEEE.

关键词： Color image processing

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Lightweight Stereo image Super-Resolution Using Parallax Attention

Lightweight Stereo Image Super-Resolution Using Parallax Att...

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2024 International conference on Advancements in Power, Communication and Intelligent systems, APCI 2024

作者： Govind, Smriti Pradeep, R. College of Engineering Trivandrum Dept. of Electronics and Communication Engineering Kerala India Apj Abdul Kalam Technological University India

ISBN: (纸本)9798350363289

Over the past decade, advancements in cellular phone technology have drastically transformed mobile phones from mere communication devices into powerful mini-computers. The integration of high-quality cameras with smartphones has led to a surge in demand for high-resolution images captured using these compact handheld devices. Prevalent use of dual cameras in smartphones has further motivated the exploration of stereo image super-resolution (SSR) algorithms, which utilizes the complementary information provided by binocular system. In this paper, we propose a novel and simple SSR approach that incorporates depth-wise separable convolutions and a parallax attention module. The algorithm effectively employs depth-wise separable convolutions to extract features from both left and right view of stereo image pair. Additionally, the parallax attention module facilitates the integration of cross-view information to enhance the super-resolution process. By combining these techniques, the proposed SSR model, Light weight Parallax Attention Stereo image Super-Resolution(L-PASSR) achieves a light weight design while still delivering comparable results in stereo image enhancement. © 2024 IEEE.

关键词： Stereo image processing

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Fault Diagnosis of Wind Turbine Bearings Using Siamese Networks 19

Fault Diagnosis of Wind Turbine Bearings Using Siamese Netwo...

引用

IEEE 19th conference on Industrial Electronics and Applications (ICIEA)

作者： Lee, Injae Cho, HaeJun Kim, Kyungseok Lee, Hyunmin Kim, Jungchan Paik, Joonki Chung Ang Univ Dept Artificial Intelligence Seoul South Korea Chung Ang Univ Dept Image Seoul South Korea Doosan Enebil Turbomachinery Technol Dev Team Seoul South Korea

ISBN: (纸本)9798350360875;9798350360868

Bearing faults pose a significant threat to the reliability and efficiency of rotating machinery systems, particularly in wind turbines. Traditional fault diagnosis algorithms often face limitations, such as processing single signal inputs from each sensor without considering interferences between components. To overcome these shortcomings and leverage the potential of processing multi-dimensional signal inputs, we propose SiamFD-net, a deep learning-based method. Focusing on wind turbine bearing fault diagnosis, our model employs a Siamese network architecture, which distinguishes between similar and dissimilar input pairs. This approach not only addresses the limitations of traditional algorithms but also contributes to mitigating the class imbalance problem commonly encountered in fault diagnosis. Experimental results using the Doosan Wind Turbine bearing dataset demonstrate the comprehensive view and diagnosis capability of our model. Furthermore, we validate the effectiveness of SiamFDnet using the Case Western Reserve University (CWRU) bearing fault diagnosis benchmark dataset. Our code is openly accessible at: https://***/SiamFDnet.

关键词： bearing fault diagnosis deep learning convolutional neural network siamese network

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Enhancing Structural Health Monitoring: AI-Driven image processing for Automated Crack Identification in Concrete Surfaces 5

Enhancing Structural Health Monitoring: AI-Driven Image Proc...

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5th International conference on Electronics and Sustainable Communication systems, ICESC 2024

作者： Jency, S. Ramkumar, G. Saveetha School of Engineering SIMATS Chennai Technology Department of Ece Chennai India

ISBN: (纸本)9798350379945

One of the first signs of structural deterioration is cracks in the concrete surface, which is important for maintenance because prolonged exposure will seriously harm the environment. The highly regarded method for inspecting cracks is manual inspection. During the handwritten inspection, the irregularities' conditions are noted and a hand-drawn illustrate of the crack is created. One of the main problems on large construction sites is the degradation of structures as a result of cracks, especially in cases where inspection by hand is not feasible. Thus, an alternative called automatic image-based crack detection is suggested. The primary goal of this research work is to use artificial intelligence-based image processing techniques to identify and categorize cracks. Here, this study proposes five modules. The first module collects the dataset of crack images;the second module applies a pre-processing method to filter the data;the third module divides the data into training and testing portions;and the fourth module implements a model that uses both deep learning, such as the Mobile-net algorithm, and machine learning, such as the Random Forest algorithm, for testing and training the data. The last part is the prediction module, which forecasts whether a crack image will be positive or negative. Lastly, this study compares the accuracy score, recall, precision, loss graph, F1 score value, and algorithms for machine learning along with deep learning. The system performed better, as demonstrated by the experimental results. © 2024 IEEE.

关键词： Random forests

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