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

共 13718 条记录，以下是1501-1510 订阅

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Design of Cloud Platform for Efficient Remote Sensing Big Data processing Based on Open Stack

Design of Cloud Platform for Efficient Remote Sensing Big Da...

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

作者： Yan Huang Weimeng Zhang Xi’an Traffic Engineering Institute Xi’an Shaanxi China Xi’an Technical School Xi’an Shaanxi China

The rapid preparation of computing resources is an important step to realize efficient remote sensing image processing. It takes a lot of time for researchers to prepare hardware resources and deploy computing framework. It often mistakes in the preparation and careless deployment of the framework which can lead to high research costs. To solve this problem, this paper uses Open Stack to virtualize hardware resources and uses Linux Shell scripting language to quickly prepare and deploy the Spark parallel computing environment. In this paper, based on the remote sensing image change detection method of connection condition, for example, the data processing algorithm in advance import platform algorithms library, component library, used to support the remote sensing image change detection of business flow of creation and recognition, resources rapid preparation, execution and presentation of the results of the algorithm and implement of massive remote sensing image change detection and efficient. It effectively improves the efficiency of remote sensing data management.

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Heterogeneous FPGA-based System for Real-Time Drowsiness Detection 17

Heterogeneous FPGA-based System for Real-Time Drowsiness Det...

引用

17th conference on Ph.D Research in Microelectronics and Electronics (PRIME)

作者： Migali, Andrea Spagnolo, Fanny Corsonello, Pasquale Univ Calabria Dept Informat Modeling Elect & Syst Engn I-87036 Arcavacata Di Rende Italy

ISBN: (纸本)9781665467001

Drowsiness detection is a key feature in modern Advanced Driver Assistance systems (ADAS). State-of-the-art approaches rely on machine learning techniques and neural networks to monitor unusual movements of the head and eyes activities. Unfortunately, due to their computationally intensive operations, integrating such algorithms in real-time and low-power operating scenarios, like automotive applications, is still quite challenging. This paper proposes an efficient hardware architecture for real-time drowsiness detection based on monitoring the driver's eye blinking behaviour through the PERcentage of eye CLOSure (PERCLOS) metric. Experimental results obtained on the Xilinx Zynq XC7Z020 FPGA SoC show that the proposed system is up to 33.3 times faster and 2.6 times less area consuming than state-of-the-art competitors.

关键词： Drowsiness detection ADAS real-time image processing FPGA

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Application Research of image Recognition Algorithm Based on Deep Learning in Intelligent Monitoring System

Application Research of Image Recognition Algorithm Based on...

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IEEE International conference on Civil Aviation Safety and Information Technology (ICCASIT)

作者： Liang Pang Guorui Kan School of Economics and Management Hubei University of Science and Technology Xianning Hubei China Kaixuan Mingdi No. 7 Jing’an Road Wuchang District Wuhan Hubei China

ISBN: (数字)9798350389418

ISBN: (纸本)9798350389425

With the extensive implementation of smart surveillance systems in the security domain, enhancing image recognition accuracy and efficiency has emerged as a crucial challenge. In this research, an image identification algorithm rooted in deep learning is proposed to boost the object detection and recognition capabilities of intelligent monitoring systems. During the system simulation phase, a convolutional neural network (CNN) model is established to train and test a substantial volume of surveillance image data. Comparative experiments validate the superior performance of the proposed method in complex environments. The findings demonstrate that this approach can greatly enhance the monitoring system’s target identification precision across different scenarios while maintaining excellent processing speed. The in-depth analysis of simulation data further confirms the reliability and practicality of the system, offering technical backing for the evolution of intelligent monitoring systems. This work not only details the algorithm design process but also validates the simulation outcomes through precise data analysis, indicating that this algorithm’s application in smart surveillance systems holds broad prospects and significant practical value.

关键词： Deep learning image recognition Data analysis Surveillance Heuristic algorithms Object detection Data models systems simulation Security Convolutional neural networks

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algorithms of Signal processing in the System for High-Speed Corrugation Monitoring of Rails

Algorithms of Signal Processing in the System for High-Speed...

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International Youth conference on Radio Electronics, Electrical and Power Engineering (REEPE)

作者： Vasiliy Ya. Koluchkin Nikita E. Marenov Bauman Moscow State Technical University Moscow Russian Federation

In real operating conditions of the control systems based on the parallax method with structured laser illumination, due to background solar illumination, nonlinear distortions of signals, known as the blooming effect, may occur. Such distortions lead to a decrease in the accuracy of the systems of operational control of straightness of railroad rails based on the above method. The purpose of the research presented in this paper is to evaluate the effectiveness of the subpixel peak position refinement algorithm under normal conditions and under background illumination conditions in an operational high-precision inspection system. As a result of numerical experiments of the rail surface straightness control system model, the effectiveness of subpixel refinement algorithms is investigated in the presence of internal noise of the image registration device, as well as in the presence of distortions caused by the blooming effect.

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International conference on Intelligent Emerging Methods of Artificial Intelligence and Cloud Computing, IEMAICLOUD 2021

International Conference on Intelligent Emerging Methods of ...

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International conference on Intelligent Emerging Methods of Artificial Intelligence and Cloud Computing, IEMAICLOUD 2021

ISBN: (纸本)9783030929046

The proceedings contain 67 papers presendted at a virtual meeting. The special focus in this conference is on Intelligent Emerging Methods of Artificial Intelligence and Cloud Computing. The topics include: False Alarm Detection in Wind Turbine Management by K-Nearest Neighbors Model;classification Learner Applied to False Alarms for Wind Turbine Maintenance Management;agricultural image Analysis on Wavelet Transform;deep Learning algorithms;deep Reinforcement Learning;different Texture Segmentation Techniques: Review;Fully Protected image Algorithm for Transmitting HDR images Over a WSN;gabor Wavelets in Face Recognition and Its Applications;a Hybrid Model Based on Behavioural and Situational Context to Detect Best Time to Deliver Notifications on Mobile Devices;harris Corner Detection for Eye Extraction;human Computer Interface Using Electrooculogram as a Substitute;image Fusion Using Wavelet Transforms;review on Traction Control System;various algorithms Used for image Compression;wavelet Transformation for Digital Watermarking;singular Value Decomposition Based image Compression;wavelet Transform for Signature Recognition;object Detection with Compression Using Wavelets;a Review on Deep Learning Models;smart IoT System for Chili Production Using LoRa Technology;real Time Based Target Detection Method;business Analytics: Trends and Challenges;home Automation: IoT;A Review on Impact of COVID-19 on E-Commerce;data Security Techniques in Cloud Computing;radio Frequency Identification Technology Used to Monitor the Use of Water Point for Grazing Cattle;a Study of Continuous Variable Transmission;review on Color image processing Techniques;building Demolition Technique;method of Noise Control for Building;peculiarities of image Recognition by the Hopfield Neural Network;preface;introduction.

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ORTPiece: An ORT-Based Turkish image Captioning Network Based on Transformers and WordPiece

ORTPiece: An ORT-Based Turkish Image Captioning Network Base...

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IEEE Signal processing and Communications Applications (SIU)

作者： Asim Ersoy Olcay Taner Yıldız Sedat Özer Özyeğin University

Recent transformers-based systems are advancing image captioning applications. However, those works have been mainly applied to English-based image captioning problems. In this paper, we introduce a transformers-based Turkish-based image captioning algorithm. Our proposed algorithm uses appearance and geometry features from the input image and combines them along with the WordPiece embeddings to generate the Turkish-based caption. Our experimental results show improvement when compared to the other existing techniques including the original ORT and the show-and-tell algorithms.

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Adaptive Compressive Sensing for Natural images Using Schur Decomposition and Optimized Reconstruction

Adaptive Compressive Sensing for Natural Images Using Schur ...

引用

Sustainable Expert systems (ICSES), International conference on

作者： M. Aarthi Elaveini T. Deepa Department of Electronics and Communication Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Ramapuram Chennai India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

Compressive sensing (CS) is growing as an effective method for efficient imagine capture and recovery by harnessing the fundamental sparseness of natural images. The paper presents a unique framework for CS of live images employing Schur decomposition, a matrix decomposition approach that improves the detection and reconstruction processes. The suggested technique utilizes Schur decomposition to enhance the sensing matrix, facilitating more effective sampling by collecting essential imagine information in both spatial and frequency domains. The Schur-based sensing matrix, in contrast to conventional random sensing matrices, is designed to use the structural characteristics of real pictures, hence enhancing the precision of image reconstruction from a reduced quantity of observations. The integrated Schur decomposition with an iterative image reconstruction algorithm, incorporating Orthogonal Matching Pursuit (OMP) promotes sparsity. The combination improves the robustness of the recovery process while reducing reconstruction artifacts. Extensive simulations demonstrate that proposed Schur-based framework achieves superior performance compared to conventional CS techniques, particularly in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM). The proposed method improves the quality of reconstructed images and expedites the convergence of the reconstructing technique, hence reducing computational complexity. The framework is assessed over several natural imagine datasets, demonstrating substantial improvements in image quality and computing efficiency resulting in a PSNR of 37.263 dB and SSIM 0.813 for CAT image. The Schur decomposition method for CS is an effective solution for applications necessitating quick picture capture and high-quality reconstruction, including medical imaging, remote sensing, and real-time image processing systems.

关键词： image quality PSNR Matching pursuit algorithms Real-time systems Iterative algorithms Sensors Matrix decomposition image reconstruction Compressed sensing Remote sensing

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ISUKAT: Capture, Measure, and Know Your Perfect Shoe Size Using image Recognition 4

ISUKAT: Capture, Measure, and Know Your Perfect Shoe Size Us...

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4th International conference on Intelligent Cybernetics Technology and Applications, ICICyTA 2024

作者： Francisco, Andrea Elaine Jumamoy, Jowan Gavriel J. Lim, Bryle Elys N. Centeno, Criselle J. Sison, Ariel Antwaun Rolando C. Marcelo, Erwin D. Dioses, Raymund M. College of Information Systems and Technology Management Pamantasan ng Lungsod ng Maynila Manila Philippines

ISBN: (纸本)9798331506490

iSUKAT represents a groundbreaking web-based application that harnesses advanced image recognition technology, specifically Roboflow 3.0 Instance Segmentation with a COCO-seg checkpoint, to revolutionize foot measurement and shoe selection. By employing sophisticated machine learning algorithms, the system accurately captures and analyzes foot dimensions, identifying foot types and shoe brands with high precision. The application directly addresses the critical issue of improper shoe sizing, which impacts 63-72% of consumers and leads to significant discomfort and potential foot health problems. Utilizing a comprehensive evaluation methodology based on ISO 25010 quality standards, the research assessed the system's performance through quantitative metrics including Mean Average Precision for foot detection and segmentation, instance segmentation accuracy, and real-time image processing inference time. The empirical results were notably impressive, with the system achieving excellent ratings across key performance domains: Functional Appropriateness scored 3.35/4.00, Usability reached 3.26/4.00, and Reliability was evaluated at 3.31/4.00, resulting in an overall system acceptance of 3.21/4.00, which was classified as satisfactory. The application strategically addresses e-commerce shoe shopping challenges by providing personalized recommendations based on precise foot measurements. However, the research also revealed nuanced performance insights, with only 11.6% of users strongly agreeing on measurement precision, while 40.5% expressed concerns about measurement accuracy. Despite these challenges, the study ultimately underscores iSUKAT's transformative potential in digital footwear selection, presenting an innovative solution that seamlessly integrates advanced image recognition, machine learning, and user-centric design to dramatically enhance the online shoe shopping experience. © 2024 IEEE.

关键词： Medical problems

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An image processing Based Approach Towards Recognition of Underground Cable Defects

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

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Smart Technologies for Power and Renewable Energy (SPECon), International conference on

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

ISBN: (数字)9798350383041

ISBN: (纸本)9798350383058

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.

关键词： Manufacturing industries Fault diagnosis Insulation Renewable energy sources Manufacturing processes image recognition Power transmission

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Detection of Stages of Cervical Cancer using Deep Learning

Detection of Stages of Cervical Cancer using Deep Learning

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Artificial Intelligence and Smart systems (ICAIS), International conference on

作者： Priyadarshini Chatterjee Computer Science and Engineering B V Raju Institute of Technology Narsapur Telengana India

Deep Learning approaches have gained importance recently specially in the field of medical image processing. They are widely used for image segmentation, analysis of an image and also used as image preprocessing tool. Amongst the other cancer, cervical cancer is one of the deadliest. Moreover, if they are not detected earlier can be fatal. Although, there are many algorithms that help to detect cancer, but their accuracy varies. The accuracy of the stages of cancer detection depends mostly on segmentation. More the accurate is the segmentation, the accuracy of the stages of cancer detection will increase. This article proposed an algorithm to segment a crowded nucleus using a neural network after preprocessing of the image. Then the cytoplasm of the nucleus is sent for feature extraction which predicts the stages of cancer. The proposed method compares the algorithm on terms of false positive, false negative, true positive and true negative. The results are tabulated in the result section of the paper.

关键词： Deep learning image segmentation Neural networks Prediction algorithms Cancer detection Feature extraction Biomedical image processing

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