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

共 12899 条记录，以下是971-980 订阅

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A Study of Machine Learning and Deep Learning algorithms for Deep Fake images Prediction

A Study of Machine Learning and Deep Learning Algorithms for...

引用

Data Science and Network Security (ICDSNS), International conference on

作者： Padmavati E. Gundgurti K. Baburao Department of Computer Science and Engineering CMR University Bengaluru India

ISBN: (数字)9798350373110

ISBN: (纸本)9798350373127

Recent developments in synthetic data have made it feasible to develop high-quality images in a way that humans cannot recognize the difference between images generated through Artificial Intelligence (AI) and real-time images. AI and Machine Learning (ML) in general have shown impressive results recently in a variety of tasks including natural image processing, particularly with the development of Deep Learning (DL). Particularly, image classification in medical images requires a high degree of accountability and reliability. However, previous researches require further analysis, often with a better understanding of the image classification process. To identify and categorize images as either synthetic or natural, this review offers a variety of ML and DL techniques. This survey looks at developments in digital imagery to strengthen these AI systems and increase their ability to perform effectively.

关键词： Deep learning Surveys Machine learning algorithms Reviews Network security Prediction algorithms Real-time systems Reliability image classification Synthetic data

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Real-Time Dehazing and Defogging: A Comprehensive Analysis for Single image Haze and Fog Removal

Real-Time Dehazing and Defogging: A Comprehensive Analysis f...

引用

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

作者： Apurva Kumari A. Pavan Kumar B.V.S Tarun Teja Department of Electronics and Communication Engineering B V Raju Institute of Technology Telangana

ISBN: (数字)9798350382693

ISBN: (纸本)9798350382709

Implementing image dehazing and defogging on a Field Programmable Gate Array (FPGA) offers efficiency. Dehazing an image becomes particularly challenging in the presence of fog or haze. However, employing a dark channel prior to dehazing allows the removal of haze particles from the image. Different modules are used in this process, such as Dynamic Atmospheric Light Estimation (DALE), Scene Recovery (SR), and Transmission Map Estimation (TME). The FPGA runs these modules in hardware and produces effective outcomes by employing image processing algorithms. In this case, using FPGA technology offers a number of benefits. The dehazing process can be accelerated by using FPGA's built-in parallel processing capabilities to execute numerous operations at once. Furthermore, FPGA implementations provide better throughput and reduced latency in comparison to conventional approaches, making them well-suited for real-time applications such as image dehazing and D

关键词： Estimation Transportation Parallel processing Logic gates Throughput Real-time systems Safety

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A HVDC System Fault Diagnosis Method Based on PSO-LightGBM Algorithm

A HVDC System Fault Diagnosis Method Based on PSO-LightGBM A...

引用

International conference on Vision, image and Signal processing (ICVISP)

作者： Zhanjun Huang Maning Zhang Jun Xie Yiting Wang School of Aeronautics Northwestern Polytechnical University Xi'an China National Key Laboratory of Aircraft Configuration Design Xi'An China

ISBN: (数字)9798331541460

ISBN: (纸本)9798331541477

Due to the complexity of their structure and the particularity of their application environments, aircraft High-Voltage Direct Current (HVDC) systems are prone to faults, with inter-module failures complicating fault diagnosis. To address this issue, a fault diagnosis method for HVDC systems has been developed. Feature extraction methods were designed for the rectifier, BUCK converter, and inverter, respectively, with the sum-to-amplitude ratio of signals selected as a feature for the rectifier; multi-scale skewness was proposed for the BUCK converter; and the ratio of the signal's average to peak absolute value was chosen for the inverter. Subsequently, the PSO-LightGBM algorithm was proposed, which employs the LightGBM algorithm for classification and utilizes a particle swarm algorithm to optimize the parameters of the LightGBM, establishing the optimal model. The experimental results demonstrate that the proposed method can accurately achieve fault diagnosis in HVDC systems.

关键词： Fault diagnosis Buck converters HVDC transmission Atmospheric modeling Signal processing algorithms Rectifiers Feature extraction Inverters Classification algorithms Aircraft

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image processing-Based Dust Detection for Solar Panels

Image Processing-Based Dust Detection for Solar Panels

引用

Smart systems for Electrical, Electronics, Communication and Computer Engineering (ICSSEECC), International conference on

作者： Sree Sowndarya Barani K Keerthana S Hariharasudhan S Mohana SundaramN B.E. Electrical and Electronics Engineering Kumaraguru College of Technology Tamilnadu India Electrical and Electronics Engineering Kumaraguru College of Technology Tamilnadu India

ISBN: (数字)9798350378177

ISBN: (纸本)9798350378184

This study focuses on dust detection in solar panels by utilizing image processing techniques. Dust detection helps in forecasting the maintenance needs and ensures system reliability. Inefficient maintenance practices of solar panels lead to increased downtime and decreased energy production. Implementation of dust detection holds importance in ensuring consistent power output. In the existing methodologies, Artificial Neural Network algorithms are implemented for dust detection. Artificial Neural Network algorithms show a considerably lower accuracy in image processing due to overfitting and complexity of image data. In the proposed system, a comparative analysis is performed by implementing Multi-Layer Perceptron technique and Dense Net algorithm. Using Multi-Layer Perceptron, a Sequential model is built with one flatten layer and two dense layers. It shows an accuracy of 88%. The Dense Net algorithm was also implemented on the processed image dataset and it shows an accuracy of 98%. This helps in increasing the accuracy and minimizing error. The Thonny IDE is employed for the implementation of the models, with validation conducted using hardware components such as Raspberry Pi, solar panels, and camera modules. Real-time images are captured to assess the performance. The utilization of the Convolution Neural Network algorithm, specifically Dense Net, for dust detection has led to enhanced system accuracy and efficiency.

关键词： Temperature sensors Temperature distribution Accuracy image processing Fault detection Voltage Hardware

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Research on the Detection Algorithm of Rural Road Surface Damage Based on image processing 2nd

Research on the Detection Algorithm of Rural Road Surface Da...

引用

2nd International conference on image, Vision and Intelligent systems, ICIVIS 2022

作者： Ye, Pei Jiang, Tao Zhan, Hui Wuchang Institute of Technology HB WuHan China Wuhan University of Technology HB WuHan China

ISBN: (纸本)9789819909223

China's new rural construction project has developed rapidly. The rural landscape has changed a lot. The scale of the rural roads is getting larger and larger. The maintenance cost of rural road diseases is also very large. People use computer technology and artificial intelligence technology to improve the detection effect of road viruses in rural areas. This paper has collected a large number of pictures of rural road surface diseases. The types of disease include: heet damage, potholes, cracks and peeling. We use the SSD algorithm to process and identify these images. with some results, We have achieved some results, but the overall identification rate is not particularly high. To further improve the identification effect, we propose an improved SSD algorithm. We found that the improved SSD algorithm also had some results in the identification of rural road surface diseases. We compare the two algorithms, and we find that improving the SSD algorithm has more advantages. The improved SSD algorithm has a higher recognition rate and a faster recognition speed. We believe that this algorithm can provide a new idea for rural pavement disease identification. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： image processing

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RailSet: A Unique Dataset for Railway Anomaly Detection 5

RailSet: A Unique Dataset for Railway Anomaly Detection

引用

5th IEEE International conference on image processing Applications and systems (IPAS)

作者： Zouaoui, Arij Mahtani, Ankur Hadded, Mohamed Amine Ambellouis, Sebastien Boonaert, Jacques Wannous, Hazem FCS Railenium Famars France Gustave Eiffel Univ Lille France Univ Lille Ctr Digital Syst IMT Nord Europe Inst Mines Tel ecom Lille France

ISBN: (纸本)9781665462198

Understanding the driving environment is one of the key factors in achieving an autonomous vehicle. In particular, the detection of anomalies in the traffic lane is a high priority scenario, as it directly involves vehicle's safety. Recent state of the art image processing techniques for anomaly detection are all based on deep learning of neural networks. These algorithms require a considerable amount of annotated data for training and test purposes. While many datasets exist in the field of autonomous road vehicles, such datasets are extremely rare in the railway domain. In this work, we present a new innovative dataset relevant for railway anomaly detection called RailSet. It consists of 6600 high-quality manually annotated images containing normal situations and 1100 images of railway defects such as hole anomaly and rails discontinuity. Due to the lack of anomaly samples in public images and difficulties to create anomalies in the railway environment, we generate artificially images of abnormal scenes, using a deep learning algorithm named StyleMapGAN. This dataset is created as a contribution to the development of autonomous trains able to perceive tracks damage in front of the train. The dataset is available at this link.

关键词： image generation Anomaly dataset Railway tracks anomaly Autonomous train Generative and Adversarial Neural Networks

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A Survey on Music Genre Classification Using Multimodal Information processing and Retrieval

A Survey on Music Genre Classification Using Multimodal Info...

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2023 IEEE International conference on Recent Trends in Electronics and Communication, ICRTEC 2023

作者： Abhyankar, Shreya G. Bharadwaj, Shashank S. Rani, G. Shobha Karigiri, Pruthvi G. Srikanth, Sahana Gurugopinath, Sanjeev Pes University Department of Electronics and Communication Engineering Bengaluru560085 India

ISBN: (纸本)9798350396195

Musical descriptors play a very crucial role in automatic music genre classification. With the increase in digitized music, query and retrieval of musical data has always been subjective and controversial. More often, a musical clip is often spread across the genres, making the classification process very challenging. This paper presents a contemporary survey of the recent advances made in the classification of music genre using different machine learning and deep learning algorithms. Specifically, it looks into the recent developments in the area of genre classification based on audio based descriptors, image based descriptors, and other descriptors which include album cover art, brain signals, lyrics and musical instrument digital interface data. © 2023 IEEE.

关键词： Music

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Enhancing Scene Simplification and Optimization for Retinal Prosthesis Platform 5

Enhancing Scene Simplification and Optimization for Retinal ...

引用

5th International IEEE Novel Intelligent and Leading Emerging Sciences conference, NILES 2023

作者： Holiel, Heidi Ahmed Fawzi, Sahar Ali Al-Atabany, Walid Nile University Medical Imaging and Image Processing Research Group Center for Informatics Science Sheikh Zayed City Egypt Cairo University Systems and Biomedical Engineering Department Giza Egypt Helwan University Biomedical Engineering Department Helwan Egypt

ISBN: (纸本)9798350381030

Retinal prostheses are designed to aid individuals with retinal degenerative conditions such as Retinitis Pigmentosa (RP) and Age-related Macular Degeneration (AMD). These prostheses seek to restore vision and improve the perceived scene by stimulating degenerated retinal cells using retinal stimulating electrodes. While these electrodes allow more efficient interaction with the surroundings, they offer limited *** paper presents an innovative approach to revolutionize the visual perception of retinal prosthesis users. The key idea behind the proposed approach is to fuse information extracted from advanced Meanshift and saliency detection algorithms. This proposed approach aims to improve the perceived scene for users, leading to enhanced visual perception and increased efficiency of retinal prostheses. © 2023 IEEE.

关键词： Computer vision

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Reach-to-Grasp Motion Tracking System Using Single Smart Phone Camera 7

Reach-to-Grasp Motion Tracking System Using Single Smart Pho...

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7th International conference on Business and Industrial Research (ICBIR)

作者： Thongprasan, Sahawatchara Liangsorn, Natsakorn Raden-ahmad, Chafik Kongkaew, Kotchakorn Tretriluxana, Jarugool Tretriluxana, Suradej King Mongkuts Inst Technol Dept Elect Engn Sch Engn Bangkok Thailand Mahidol Univ Fac Phys Therapy Motor Control & Neural Plast Lab Bangkok Thailand

ISBN: (纸本)9781665494755

Assessment of human movement is necessary for physical therapy management. This article presents a development of motion tracking system for human Upper Extremity (UE) function analysis. We proposed the optical motion capture system made by a single smart phone camera. It was used to capture the Reach-to-Grasp (RTG) movement of participants in sitting position. image processing were used to detect color markers placed on chosen hand anatomical landmarks. With our simple camera calibration technique, the 3D coordinates of hand movement were obtained. Two clinical parameters, grasp aperture and hand transport velocity were computed. These results were compared with the outputs, collected at the same time, from the higher accuracy Electromagnetic Motion (EM) tracking system. Qualitatively, the result patterns from two systems were parallel to each other. Our ongoing work is to improve the algorithms according to the feedback from clinicians. This system may provide implication for physical therapist to assess the clients' movement in the clinical setting.

关键词： motion tracking system Reach-To-Grasp (RTG) movement image processing Upper Extremity (UE) smart phone camera

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Impacts of image Compression on the Detection Quality of a Novel Real-Time image processing Platform

Impacts of Image Compression on the Detection Quality of a N...

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International conference on Intelligent Transportation

作者： Jannik Mehrke Georg Volk Yannik Stumpp Oliver Bringmann Anestis Terzis Institute of Communication Technology Ulm University of Applied Sciences Dept. of Computer Engineering University of Tübingen

Cloud-based data processing latency mainly depends on the transmission delay of data to the cloud and the used data processing algorithm. To minimize the transmission delay, it is important to compress the transferred data without reducing the quality of the data. When using data compression algorithms, it is important to validate the impact of these algorithms on the detection quality. This work evaluates the effects of image compression and transmission over wireless interfaces on state of the art neural networks. Therefore, a modern image processing platform for next generation automotive processing architectures, as used in software defined vehicles, is introduced. The impacts of different image encoders as well as data transmission parameters are investigated and discussed.

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