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

共 16440 条记录，以下是2071-2080 订阅

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Removing Reflective Flare in Real-World Conditions

Removing Reflective Flare in Real-World Conditions

引用

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...

引用

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...

引用

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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image Enhancement Method for Arc Welding Process Monitoring in the Present of Strong Arc Light Interference 7th

Image Enhancement Method for Arc Welding Process Monitoring ...

引用

7th International conference on Nanomanufacturing, nanoMan2021

作者： He, xinxin Liu, Bin Yang, Fan Wang, Zhongya Wen, Yaxin Zhang, Baofeng School of Electrical and Electronic Engineering Tianjin University of Technology Tianjin China Tianjin Key Laboratory for Control Theory and Applications in Complicated Systems Tianjin China Co. Ltd. Tianjin China University of Technology Tianjin China

ISBN: (纸本)9789811919176

The application of vision technology plays a significant role in the development of intelligent automatic welding. However, the welding process would produce strong arc light. The arc light seriously reduces the imaging quality of the vision system. In this case, the function of vision auxiliary would fail. The operation of a suitable image enhancement algorithm is capable of solving the problems caused by the strong arc light, such as nonuniform brightness, the loss of edges and low signal-to-noise. In this paper, image enhancement algorithm was studied to solve the imaging interference caused by the strong arc light. According to the characteristic of captured image, the advantage of LIME algorithm was analyzed. Then suitable filtering method was introduced into LIME to improve the enhancement. Experimental results showed that the algorithm could balance the global brightness and suppress the intensity of the strong light area. Furthermore, the original object contour details could be restored to optimize the visual effect. By investigating the effect of various low-light image enhancement algorithms from both subjective and objective aspects, the combined algorithm was proved to be effective. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： image enhancement

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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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A Method for Generating Sea Clutter Data Based on Variational Autoencoder

A Method for Generating Sea Clutter Data Based on Variationa...

引用

IEEE International conference on Signal and image processing (ICSIP)

作者： xingyu Deng Bingwei Hui xing Han Fei Gao Dawei Duan Systems Engineering Department Xi'an Electronic Engineering Research Institute Xi'an China National Key Laboratory of Automatic Target Recognition National University of Defense Technology Chang Sha China 91001Unit The Chinese People's Liberation Army Beijing China

ISBN: (数字)9798350350920

ISBN: (纸本)9798350350937

The acquisition of sea clutter data faces significant challenges such as high collection costs and long cycles, which have impeded research on sea clutter characteristics and hindered the development of scientific research based on sea clutter data. This study proposes a method for generating sea clutter data based on a Variational Autoencoder (VAE). By extending the traditional VAE framework, a network model capable of generating one- dimensional sea-clutter data was established. The model was trained using a dataset of sea clutter data collected in practice, and comparative analyses were conducted on the amplitude distribution characteristics and autocorrelation properties of the generated sea clutter data. Experimental results demonstrate that this method not only generates a large quantity of diverse sea clutter data closely resembling real sea clutter data but also has the potential to provide more effective data support for the study and application of sea clutter characteristics.

关键词： Analytical models Costs Sea measurements Data models Autocorrelation Clutter image reconstruction

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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...

引用

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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Classification of image Classes Based on the PCA Algorithm Optimized by the KNN Algorithm Improved by Genetic algorithms

Classification of Image Classes Based on the PCA Algorithm O...

引用

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

作者： Constantin Doru Bălcău Costel Department of Mathematics and Computer Science The National University of Science and Technology POLITEHNICA Bucharest Piteşti Univ. Centre România

In this paper we propose an optimized version of the PCA algorithm by using genetic algorithms and the KNN technique with applications in the classification of image classes. The algorithm are developed by optimizing the images projection stage based on the eigenvectors that ensure the extraction of essential features from the data. In that stage, the optimal value regarding the size of the projection space of the initial images is determined by using a genetic algorithm and and the improved KNN algorithm regarding the key parameter represented by the number of nearest neighbors used in the testing stage. For the validation and testing of the developed algorithms, the classification of face image classes corresponding to several people is considered. The proposed algorithm includes several versions, analyzed in the experimental study, depending on the similarity measure used (the Euclidian distance, Manhattan distance, Mahalanobis distance, Minkowski distance and cosine) to determine the nearest neighbors to the KNN algorithm, as well as based on the crossover recombination method chosen at the level of binary genes with a crossover point or more many crossing points. The experimental study on the methods established in the work attests good performance through the percentages of correctness, resulting in applications for recognizing people through facial images.

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