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

共 12901 条记录，以下是1181-1190 订阅

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Two-step Method Grid Map Merging Based on Corner Points 41

Two-step Method Grid Map Merging Based on Corner Points

引用

第41届中国控制会议

作者： Chong Chai YunZhi Huang Hefei University of Technology School of Electrical and Automation Engineering

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Multi-robot SLAM(Simultaneous Localization and Mapping) can be used in many collaboration-based operations,such as military rescue *** merging is the most obvious solution to the multi-robot ***,traditional corner-based map fusion algorithms failed in complex *** paper proposes a corner-based two-step method for map merging in complex ***,the map is preprocessed,including orthogonal rotation,filtering,and edge *** corners are identified by Harris Corner detection *** constructing a convex quadrilateral based on the corner points, the overlapping area can be selected for the first ***,the intersection of the obtained quadrilateral diagonals is used as a new feature point,and a triangle is constructed for matching to obtain the final overlapping ***,the rotation angle and displacement can be obtained by calculating the transformation *** results show that this method can solve the complex grid map merging problem effectively.

关键词： Multi-robot SLAM Map merging image processing Two-step method

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Real-Time Car Parking Management System

Real-Time Car Parking Management System

引用

Intelligent algorithms for Computational Intelligence systems (IACIS), International conference on

作者： Ramachandra A C H S Jagadeesh Sanjay J C Vinay D H Siddesh H K Department of EEE Nitte Meenakshi Institute of Technology Bengaluru India Department of ECE Atria Institute of Technology Bengaluru India Nitte Meenakshi Institute of Technology Bengaluru India

ISBN: (数字)9798350360660

ISBN: (纸本)9798350360677

Urban areas in countries like India face significant challenges with traffic congestion and parking management, leading to increased fuel consumption, air pollution, and inefficiencies in transportation. We propose Real-Time Car Parking Management System (RTCPMS) to address the issues by developing a smart parking system that leverages image processing with cameras and utilizes microcontrollers for wireless control of peripherals such as gates, indicating LEDs, and LCD displays. The work integrates image processing techniques to monitor parking space occupancy using cameras installed at parking locations. The microcontroller, equipped with Wi-Fi capabilities, is employed to wirelessly control various peripherals within the parking system. Upon approaching a parking location, users receive real-time information on parking availability via LCD displays, while gate access and indication of available spaces are facilitated through LEDs. The implementation of the system accurately detects parking space occupancy, providing users with up-to-date information on available parking spots. By providing real-time information to users and enabling wireless control of parking peripherals, the system contributes to reducing congestion in parking area, minimizing fuel consumption, and promoting environmental sustainability in urban areas. The results obtained during the process are better compared to the existing.

关键词： Wireless communication Microcontrollers image processing Urban areas Aerospace electronics Logic gates Light emitting diodes Real-time systems Liquid crystal displays Fuels

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EM2-YOLO: Lightweight Remote Sensing image Detection 5th

EM2-YOLO: Lightweight Remote Sensing Image Detection

引用

5th International conference on 3D Imaging Technologies—Multidimensional Signal processing and Deep Learning, 3DIT-MSP and DL 2023

作者： Liu, Xinyi Song, Yaolian Wang, Can School of Information Engineering and Automation Kunming University of Science and Technology Yunnan650500 China

ISBN: (纸本)9789819751808

Remote sensing photographs have a wealth of information, and object detection methods are crucial in this. On mobile and embedded platforms, deep learning-based target identification algorithms are challenging to implement. Additionally, the issues of delayed target detection and poor accuracy of small target detection for remote sensing photos must be resolved immediately. To address these issues, we provide EM2-YOLOv4, a simple method for recognizing remote sensing images. MobileNetv2 acts as the backbone network to reduce model complexity, while depth-separable convolution enhances detection speed. Additionally, we introduce a feature enhancement module and an Efficient Channel Attention (ECA) module to improve model accuracy. To address the issue of unequal assignment of positive and negative samples, we incorporate Focal Loss into the loss function. Experimental demonstrate that the model can perform detection at 45 frames per second with an average accuracy of 94.11%. The model has been significantly reduced, as seen by the fact that it is only 48 MB in size, but it still maintains good detection accuracy and accelerates detection. The model is ideal for usage on embedded devices because of this feature. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Optical remote sensing

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Removing Reflective Flare in Real-World Conditions

Removing Reflective Flare in Real-World Conditions

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IEEE International conference on image processing

作者： Fengbo Lan Chang Wen Chen The Hong Kong Polytechnic University

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

The increasing prevalence of mobile devices has led to significant advancements in mobile camera systems and improved image quality. Nonetheless, mobile photography still grapples with flare corruptions such as reflective flare. The absence of a comprehensive real image dataset tailored for mobile phones hinders the development of effective flare mitigation techniques. To address this issue, we present a novel real image dataset specifically designed for mobile camera systems, focusing on flare removal. Capitalizing on the distinct properties of real images, this dataset serves as a solid foundation for developing advanced flare removal algorithms. The dataset comprises over 1,100 pairs of high-quality, full-resolution images for reflective flare, which generate 2,200 paired patches, ensuring broad adaptability across various imaging conditions. Experimental results demonstrate that networks trained with synthesized data struggle to cope with the complex lighting settings present in this real image dataset. Our dataset is expected to enable an array of new research in flare removal and contribute to substantial improvements in mobile image quality, benefiting mobile photographers and end-users alike.

关键词： image quality Photography image recognition Prevention and mitigation Lighting Focusing Cameras

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Optimization of Coupled ARX-Laguerre Multimodel Parameters Using Genetic Algorithm

Optimization of Coupled ARX-Laguerre Multimodel Parameters U...

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International conference on systems and Control (ICSC)

作者： Hajer Benamor Marwa Yousfi Larbi Chrifi Alaoui Tarek Garna Laboratory of Automatic Signal and Image Processing (LARATSI) National Engineering School of Monastir Monastir Tunisia Laboratory of Innovative Technology (LTI UR-UPJV 3899) University of Picardie Jules Verne Amiens France Higher Institute of Applied Science and Technology of Sousse Sousse Tunisia

ISBN: (数字)9798331531812

ISBN: (纸本)9798331531829

This work treats parametric optimization of nonlinear systems using genetic algorithms. The authors focus, in this paper, in combining the ARX-Laguerre model and the multimodel approach with its coupled configuration to develop a model named the coupled ARX-Laguerre multimodel. The proposed model is characterized by its Laguerre poles, Fourier coefficients and weighting functions parameters. The complexity of the model is tightly related to the optimal identification of the Laguerre poles characterizing the model. The authors propose an optimization algorithm of the multimodel parameters (Laguerre poles and weighting functions parameters) based on the genetic to guarantee a significant parametric number reduction with regards to the classic ARX multimodel.

关键词： Uncertainty Solids Genetics Control systems Chemical reactions Iterative algorithms Complexity theory Nonlinear systems Optimization Genetic algorithms

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Parameter Blending For Multi-Camera Harmonization For Automotive Surround View systems

Parameter Blending For Multi-Camera Harmonization For Automo...

引用

Acoustics, Speech, and Signal processing Workshops (ICASSPW), IEEE International conference on

作者： Yuzhuo Ren Yining Deng David Pajak Robin Jenkin Niranjan Avadhanam Varsha Hedau NVIDIA Santa Clara CA USA

ISBN: (数字)9798350374513

ISBN: (纸本)9798350374520

In a surround view system, the image color and tone captured by multiple cameras can be different due to cameras applying auto white balance (AWB), global tone mapping (GTM) individually for each camera. The color and brightness along stitched seam location may look discontinuous among multiple cameras which impacts overall stitched image visual quality. To improve the color transition between adjacent cameras in stitching algorithm, we propose harmonization algorithm which applies before stitching to adjust multiple cameras’ color and tone so that stitched image has smoother color and tone transition between adjacent cameras. Our proposed harmonization algorithm consists of AWB harmonization and GTM harmonization leveraging image Signal Processor (ISP)’s AWB and GTM metadata statistics. Experiment result shows that our proposed algorithm outperforms global color transfer method in both visual quality and computational cost.

关键词： Visualization Runtime image color analysis Signal processing algorithms Metadata Signal processing Cameras

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Design of a people access control system using the ESP32 module and Internet of Things for a sanitory facility in a shopping mall

Design of a people access control system using the ESP32 mod...

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2022 IEEE Engineering International Research conference, EIRCON 2022

作者： Casas Sanchez, Haggi Loayza Apena, Jean Palomino, Joel Paiva-Peredo, Ernesto Universidad Tecnológica Del Perú Faculty of Systems and Electronic Engineering Lima Peru

ISBN: (数字)9781665450829

ISBN: (纸本)9781665450829

Access control and management of customer in shopping centers is an important task. In this context, some shopping centers implemented payment system to access sanitary facilities. The collected money helps to keep the installations clean. According to managers the monetary report of workers is unprecise. To contrast the inaccurate reports, this research describes the design of a system to manage the data of people that access the toilets using the Internet of Things. The ESP32CAM module was programmed to capture images of people walking inside and outside of the facility. With the use of image processing and decision algorithms, the number of people that access the facility can be managed remotely through a web server. For this purpose, the information is sent to a cloud server using the Thingspeak platform. The system is design to register data from a triple access system where people can pay directly;show a receipt of consumption in the stores;or workers can show their identifications with RFID. © 2022 IEEE.

关键词： image processing

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Application of image Recognition algorithms Based on Deep Learning in the Field of Communication Science

Application of Image Recognition Algorithms Based on Deep Le...

引用

Integrated Intelligence and Communication systems (ICIICS), International conference on

作者： Yunxia Su Shanghai Publishing and Printing College Shanghai China

With the popularization of the internet, people have paid more and more attention to image processing, and deep learning has become a research hotspot. Analyzing images based on deep neural networks is a very valuable and promising direction for development. This article first introduced text representation methods and two commonly used classifiers in machine learning, namely the manual annotation model method. Then, it elaborated on the effectiveness and current application status of a fusion of multi-layer feedforward technology and convolutional neural networks for edge detection, feature extraction, and other fields. Finally, a new concept was proposed, and deep learning was applied to explain the important significance of image preprocessing in digital music file classification. Afterwards, a communication field training model based on image recognition algorithms was simulated and tested for its functionality. The test results showed that the image recognition algorithm based on deep learning had good noise processing performance during the propagation process, with an image clarity rate of over 90%. Through deep learning algorithms, automatic recognition and classification of radio signals can be achieved, including determining signal type, modulation method, frequency range, and other information. This is of great significance for radio spectrum monitoring, spectrum management, and interference source localization. By using deep learning algorithms, classification models for different types of radio signals can be trained, making the recognition of radio spectrum more accurate and reliable.

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MRI image denoising using U-Net and image processing Techniques 5

MRI image denoising using U-Net and Image Processing Techniq...

引用

5th IEEE International conference on Advances in Science and Technology, ICAST 2022

作者： Mehta, Darshil Padalia, Dishant Vora, Kush Mehendale, Ninad K.J Somaiya College of Engineering Department of Computer Engineering Mumbai India K.J Somaiya College of Engineering Department of Electronics and Telecommunication Mumbai India K.J. Somaiya College of Engineering Department of Electronics Mumbai India

ISBN: (纸本)9781665492638

Brain tumors are one of the leading causes of death, and hence it is critical to diagnose them early. MRI is the most effective diagnostic tool for detecting a tumor. However, thermal noise, temperature fluctuations, and other artifacts can generate noisy MRI scans, leading to inaccurate diagnoses. Deep learning algorithms combined with image processing techniques have aided in a variety of medical imaging tasks, including enhancing MRI images. Our work proposes a U-Net architecture with two encoder-decoder pairs for denoising MRI scans which were finely tuned on a dataset generated by injecting synthetic Gaussian noise. The model improved the Peak Signal to Noise Ratio (PSNR) from 11.90 to 30.96. The presented work also provides empirical evidence that the proposed denoising strategy enhances the prediction accuracy of brain tumors by nearly 23%. The developed denoising technique using U-Net would benefit radiologists and computer-aided diagnostic systems (CAD) in precisely diagnosing the disease by generating cleaner and clearer MRI scans. © 2022 IEEE.

关键词： image enhancement

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Digital Twin-Driven Bearing-Fault Detection in Induction Motor and Drives using Graph Sampling and Aggregation Network

Digital Twin-Driven Bearing-Fault Detection in Induction Mot...

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Haraprasad Badajena Suryanarayan Majhi Bivash Chakraborty Mamata Jenamani Aurobinda Routray Ronit Dutta Advanced Technology Development Centre Indian Institute of Technology Kharagpur India Industrial and Systems Engineering Indian Institute of Technology Kharagpur India Dept. of Electrical Engineering Indian Institute of Technology Kharagpur India

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Bearing fault diagnosis is crucial for ensuring the reliability and safety of industrial systems, particularly in preventing operational failures and maintaining product quality. Traditional signal processing methods and deep learning algorithms, while useful, often overlook the complex structural relationships within sensor data, limiting their diagnostic effectiveness. To address this, we present a novel Digital Twin-Driven Fault Diagnosis Framework that integrates graph-based learning techniques with advanced signal analysis. Our approach employs XGBoost and GraphSAGE embeddings to capture both spatial and temporal correlations within the current signals. The raw sensor data is then processed using a sliding window technique and time-frequency domain features are extracted then transformed into a graph structure that represents the intricate relationship in the signal. GraphSAGE is then applied to these graph structures, generating embeddings that enhance fault detection accuracy. Additionally, XGBoost is utilized for classification, improving the overall robustness of the system. The proposed method, deployed on edge devices, delivers real-time diagnostics, providing a scalable and efficient solution for industrial applications. Experimental results using real-world datasets demonstrate that our method significantly outperforms state-of-the-art algorithms, improving detection accuracy and Area under the curve (AUC) scores up to 97% and 99%, respectively.

关键词： Fault diagnosis Time-frequency analysis Accuracy Correlation Fault detection image edge detection Signal processing algorithms Feature extraction Real-time systems Speech processing

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