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

共 12978 条记录，以下是591-600 订阅

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Design Information Extraction and Visual Representation based on Artificial Intelligence Natural Language processing Techniques 4

Design Information Extraction and Visual Representation base...

引用

4th International conference on Computer Vision, image and Deep Learning, CVIDL 2023

作者： Liu, Dan Changzhou College of Information Technology Jiangsu China Gachon University Gyeonggi-do Korea Republic of

ISBN: (纸本)9798350326444

This research focuses on the application of artificial intelligence in the modern design field and proposes a solution to build an information visualisation design platform based on natural language processing technology. The profound impact of AI on various fields brings opportunities and challenges to the design field, which designers must be able to fully grasp in order to improve their design and work efficiency. In the research process, the framework for building a design platform based on NLP technology, an in-depth analysis of the key features of the extracted design information and their intrinsic relationships, and a rational planning and implementation validation of the process, as well as a comparative analysis in terms of computational methods for feature extraction, are presented. The visualisation platform is built based on user-oriented and data-driven principles of inter activity and diversity of expression. This study explores the applicability and effectiveness of natural language processing algorithms in the design field, expanding the scope of research on natural language processing techniques and providing an effective reference for the research and practice of combining artificial intelligence with modern design. © 2023 IEEE.

关键词： Natural language processing systems

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Development of image processing Techniques in Crack Detection and Analysis

Development of Image Processing Techniques in Crack Detectio...

引用

2023 International conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2023

作者： Al Dhafari, Luqman Sulaiman Afzal, Aftab Al Bahrani, Raid Ibrahim Al Busaidi, Yahyah Sheikh-Akbari, Akbar Hossain, Mohammad Sayeed Military Technological College Dept. Aeronautical Engineering Muscat Oman Leeds Beckett University School of Built Environment Engineering and Computing Leeds United Kingdom

ISBN: (纸本)9798350322972

Inspection of aircraft skin is required as per the Corrosion Prevention and Control Program (CPCP) to ensure aircraft structural integrity. Human visual inspection is the most widely used technique in aircraft surface inspection, according to the CPCP. Scheduled inspections and regular maintenance of an aircraft through conventional methods constitute tedious and lengthy procedures. Often the visual inspections lead to subjective judgement and do not constitute repeatability. Many automated vision-based aircraft skin inspection systems have been designed to provide a safe, quick, and accurate visual assessment over the past years. This paper presents a section of research investigating defect detecting and accurately locating the outer body of an aircraft using an Unmanned Aerial Vehicle (UAV) to capture images and digital image processing techniques to locate possible cracks. The inspection system is used to initially detect locations of cracks (defects) on an aircraft's outer skin and the detected crack is further investigated using thermal and ultrasound imaging methods. The scope of this paper includes a review of the design and development of a series of advanced dedicated image processing algorithms suitable for applying digital image processing on images captured from the outer surface of a typical aircraft fuselage. © 2023 IEEE.

关键词： Crack detection

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From Linear to Nonlinear Unfolded Condat-Vú Algorithm for Spectro-Polarimetric Hight-Constrast image Recovery

From Linear to Nonlinear Unfolded Condat-Vú Algorithm for S...

引用

European Signal processing conference (EUSIPCO)

作者： E. Chappon N. Pustelnik J. Tachella L. Denneulin A. Ferrari M. Langlois Laboratoire de Physique ENSL CNRS UMR 5672 Lyon France Laboratoire de Recherche de l'EPITA EPITA Le Kremlin-Bicêtre France Universite Côte d'Azur Observatoire de la Côte d'Azur CNRS Lab. J.-L. Lagrange France Univ Lyon Univ. Lyon 1 ENS de Lyon CNRS Centre de Recherche Astrophysique de Lyon UMR5574 Saint-Genis-Laval France

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

Studying circumstellar environments is crucial for understanding exoplanets and stellar systems. Instruments like SPHERE can extract information about these environments by leveraging advanced image reconstruction methods, possibly based on deep learning. This work focuses on unfolded proximal neural networks based on Condat- vii iterations and proposes a new nonlinear formulation. To evaluate and compare the performance of the proposed reconstruction strategies, two datasets dedicated to circumstellar environments analysis in the context of high-contrast imagery have been created offering different level of complexity in the evaluation of the performance.

关键词： Deep learning Training image color analysis Neural networks Signal processing algorithms Signal processing image reconstruction Standards Synthetic data Finite difference methods

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SMARTINDUSTRY 2024 - Proceedings of the 1st International conference on Smart Automation and Robotics for Future Industry

SMARTINDUSTRY 2024 - Proceedings of the 1st International Co...

引用

1st International conference on Smart Automation and Robotics for Future Industry, SMARTINDUSTRY 2024

The proceedings contain 16 papers. The topics discussed include: artificial intelligence for the future of construction;cobots and industrial robots;predictive maintenance for wind turbine bearings: an MLOps approach with the DIAFS machine learning model;development of an artificial intelligence tool and sensing in informatization systems of mobile robots;PCA-NuSVR framework for predicting local and global indicators of tunneling-induced building damage;design and deployment of data development toolkit in cloud manufacturing environments;research and development of image processing algorithms for effective recognition of various gestures in real time;machine learning models for the recognition of commands in smart home technologies;responsive dehydration: sensor-driven optimisation of production cycles in a solar dehydrator;and formation of the method of description and control of the relative position of the links of the upper limbs of the grip of an anthropomorphic robot.

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Fault Identification of Solar Panel Using image processing and IoT

Fault Identification of Solar Panel Using Image Processing a...

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2024 International conference on Recent Innovation in Smart and Sustainable Technology, ICRISST 2024

作者： Shanthosh, K. Sugunesh, R. Vijayabharani, D. Saranbabu, B.C. Maniraj, P. M Kumarasamy College of Engineering Department of Electrical and Electronics Engineering Karur India

ISBN: (纸本)9798350309140

Solar photovoltaic systems have emerged as a prominent source of renewable energy. However, their performance can be hindered by various factors, including faults within the PV modules or the overall system. Rapid and accurate detection of these faults is crucial for maintaining optimal energy production and ensuring system safety. This project proposes a novel approach for Solar Fault Detection using Convolutional Neural Network algorithm. The proposed system leverages the power of deep learning techniques to automatically analyse images of solar panels and identify potential faults. A comprehensive dataset of annotated solar panel images is used for training the CNN model. To account for real-world scenarios, the dataset includes images captured under various lighting conditions and angles. The CNN architecture is designed to extract intricate features from the images, allowing for precise fault identification. The model is trained on a diverse set of fault types, including but not limited to, micro cracks, hot spots, shading effects, and soiling. Transfer learning techniques are also employed to improve model performance and reduce training time. To account for real-world scenarios, the dataset includes images taken under different lighting circumstances and angles. The results demonstrate high accuracy, sensitivity, and specificity in detecting faults. Additionally, the system exhibits robustness to environmental variables such as weather conditions and time of day. In this user-friendly interface is developed to facilitate easy integration of the solar panel. The interface provides real-time feedback on the status of solar panels, highlighting any detected faults and their severity levels. This project presents a state-of-the-art approach for solar fault Detection using CNN algorithms, offers a best maintenance for the solar panel damages and fault. The proposed system holds significant potential for the solar panel damages and fault. The proposed system holds

关键词： Convolutional Neural Network Deep Learning Fault Identification insert image processing Photovoltaic systems Renewable Energy Solar Fault Detection Transfer Learning

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DISTRIBUTED RADAR AUTOFOCUS IMAGING USING DEEP PRIORS 29

DISTRIBUTED RADAR AUTOFOCUS IMAGING USING DEEP PRIORS

引用

IEEE International conference on image processing (ICIP)

作者： Mansour, Hassan Lohit, Suhas Boufounos, Petros T. Mitsubishi Elect Res Labs 201 Broadway Cambridge MA 02139 USA

ISBN: (数字)9781665496209

ISBN: (纸本)9781665496209

Antenna position ambiguity is a common problem that affects radar imaging systems that are mounted on mobile platforms. Existing approaches that aim to recover a sharp radar image despite this ambiguity aim to estimate the shift in the antenna position by modeling the radar scene as a sparse image with a small number of targets using explicit analytical models for the statistical distribution of the targets in a radar image. The radar imaging problem is then solved by alternating between estimating the radar image, followed by estimating the shift in the antenna positions, until convergence is reached. While such approaches have shown tremendous success, they still struggle to recover the true target positions and may arrive at incorrect local optima when the measurement noise level is high. In this work, we develop a data-driven learning-based strategy for modeling the image of the radar scene instead of relying on explicit analytical models. We adopt a residual Unet architecture of a neural network to act as a denoising operator which takes a backprojected radar image as input and outputs a true target image. While deep denoisers may generally result in unstable iterative algorithms, we introduce a simple filtering step that suppresses noise belonging to the null space of the radar operator from the iterates to stabilize the iterative procedure. We evaluate the effectiveness of our solution using simulated numerical experiments and demonstrate its superiority over the analytic signal prior.

关键词： Radar autofocus blind deconvolution sparse image reconstruction deep image priors block-coordinate descent

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Enhanced Model for Brain Tumor Detection Accuracy Using Inceptionresnetv2 compared to VGG19 and MobileNet Models

Enhanced Model for Brain Tumor Detection Accuracy Using Ince...

引用

2024 International conference on Innovative Computing, Intelligent Communication and Smart Electrical systems, ICSES 2024

作者： Tamilselvi, M. Ashwini, R. Ravi, S. Rajalakshmi, S. Rajkumar, Chadge Fawareh, Hamed Saveetha University Department of Ece Chennai India R.M.K College of Engineering and Technology Department of Ece Chennai India Saveetha University Department of Vlsi Microelectronics Chennai India S.A. Engineering College Department of Ece Chennai India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Zarqa University Faculty of Information Technology Software Engineering Dept Zarqa Jordan University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331543617

Brain tumors, arising from unregulated and accelerated cellular proliferation, provide considerable health hazards if not promptly addressed. Notwithstanding significant progress, precise segmentation and classification continue to pose difficulties. Artificial intelligence (AI) and medical imaging technologies have advanced recently, greatly improving illness analysis and prediction, particularly with regard to brain tumor (BT) identification. Precise classification and segmentation of tumors in the brain are essential for creating individualized treatment plans, and early detection of brain tumors is essential for improving patient prognosis and available treatments. Even with the increasing usage of Magnetic Resonance Imaging (MRI) for brain imaging and the development of AI-based detection techniques, developing a reliable and effective model to identify and classify cancers from MRI images is still a difficult task. Brain tumor analysis is essential for timely diagnosis and effective patient management. Tumor morphology, encompassing features such as dimension, position, appearance, and heteromorphic shape in medical images, poses challenges for analysis. Advancements in DL approaches for the automatic identification of tumors present in the brain using MRI data have led to the utilization of numerous algorithms for this purpose. The InceptionResNetV2 model is employed due to its efficiency and efficacy;it is a simple deep learning architecture, rendering it a suitable candidate for medical image processing applications. The proposed model attains an accuracy rate of 100% at an epoch rate of 30 and surpasses the performance of both MobileNet and VGG19 models. © 2024 IEEE.

关键词： Medical image processing

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image thresholding approaches for medical image segmentation-short literature review

Image thresholding approaches for medical image segmentation...

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2022 International conference on ENTERprise Information systems, CENTERIS 2022 - International conference on Project MANagement, ProjMAN 2022 and International conference on Health and Social Care Information systems and Technologies, HCist 2022

作者： Jardim, Sandra António, Joao Mora, Carlos Smart Cities Research Center - Ci2 Polytechnic Institute of Tomar Quinta Do Contador Tomar2300-313 Portugal Techframe - Sistemas de Informaçao SA Rua Almeida Garrett 83A Buzano Sao Domingos de Rana2785-338 Portugal

Medical image analysis is an invaluable tool in medicine. Different imaging modalities provide an effective means for mapping images that can feed machine and deep learning models which can significantly contribute to increase knowledge of diseased anatomy for medical research, being an important component in diagnosis and treatment planning. Accurate segmentation of medical images is a key step in the use of Artificial Intelligence as a tool to support physicians. Although there are many approaches to medical image segmentation, thresholding is one of the simplest methods that can be used, being of great interest to the scientific community. Over the years, several approaches to segmentation of medical images based on threshold methods have been proposed, which have evolved both in computational complexity, with shorter processing times, and in efficiency and accuracy of results. This manuscript summarizes the most common threshold-based approaches for medical image segmentation and discuss some of the methods and algorithms proposed in recent years, analyzing their advantages, disadvantages, and limitations. © 2022 The Authors. Published by ELSEVIER B.V.

关键词： Medical imaging

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Effective in-the-Moment image Compression on FPGA: A Combinatorial Method Including DWT and IWT Techniques

Effective in-the-Moment Image Compression on FPGA: A Combina...

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International conference on Advanced Computing and Communication systems (ICACCS)

作者： U. Dinesh V. Deepak Rajan S. Navaneethan Electronics and Communication Engineering Saveetha Engineering College Chennai India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384376

This paper presents an innovative way of image compression using Field-Programmable Gate Array (FPGA) implementation of the Integer Wavelet Transform (IWT) and Discrete Wavelet Transform (DWT) algorithms. For situations where resources are limited, the flexibility and adaptability offered by the FPGA architecture are ideal. Our technique strikes a compromise between compression effectiveness and image quality by utilizing DWT for multi-resolution analysis and IWT for spatial redundancy reduction. Real-time processing and resource optimization are ensured by the FPGA implementation. FPGA-optimized algorithms that tackle resource constraints are among the contributions. Evaluations demonstrate enhanced signal-to-noise ratios, compression ratios, and execution times. This study highlights how fast FPGA can compress images, especially for embedded systems and space missions. The study not only improves image compression but also highlights how FPGA can be used to increase the effectiveness of signal processing algorithms.

关键词： image coding Space missions Redundancy Signal processing algorithms Parallel processing Real-time systems Discrete wavelet transforms Field programmable gate arrays Optimization Signal to noise ratio

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CHARACTERIZATION OF TUNABLE REBOUND PROPERTIES OF MICROSTRUCTURED MAGNETOACTIVE ELASTOMERS 17

CHARACTERIZATION OF TUNABLE REBOUND PROPERTIES OF MICROSTRUC...

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ASME conference on Smart Materials, Adaptive Structures and Intelligent systems (SMASIS)

作者： Kriegl, Raphael Jezersek, Matija Kravanja, Gaia Hribar, Luka Kokot, Gasper Drevensek-Olenik, Irena Shamonin, Mikhail Ostbayerische Tech Hsch Regensburg Regensburg Germany Univ Ljubljana Fac Mech Engn Ljubljana Slovenia J Stefan Inst Ljubljana Slovenia Univ Ljubljana Fac Math & Phys Ljubljana Slovenia

ISBN: (纸本)9780791888322

We present a novel method to control the rebounding behavior of small mm-sized solid balls by employing magnetoactive elastomers (MAEs) with microstructured surfaces. An MAE is a composite material consisting of mu m-sized ferromagnetic particles dispersed in a soft elastomer (e.g., polydimethylsiloxane) matrix. In the act of rebounding, the ball hits an MAE surface and bounces back. The MAE samples contained 75 wt.% of iron. This composite material is known to respond to an applied magnetic field with increased stiffness (due to the magnetorheological effect) and plasticity. To adjust the rebound properties, the top layer of the MAE material was additionally modified by micromachining lamellar structures with different dimensions on the 100 mu m scale via laser ablation. Due to the resulting high aspect ratio, these surface structures were sensitive to the magnetic field direction. The lamellas could stand up straight or lay down flat. The rebound behavior was evaluated by using a custom build apparatus that facilitates dropping of the balls in a precise and repeatable manner. A ball was dropped from different heights. The ball trajectory was captured with a high-speed camera to investigate the rebound properties. The recorded video was processed using a custom software written in Python. The experimental procedure and data processing algorithms are presented in detail. The results for the samples with different geometrical dimensions are provided as examples. It is made evident that the magnetic field influences the rebound properties of small non-magnetic balls impinging microstructured MAE surfaces. The change in surface topography is an effective way to control the ball rebound. The fabrication flexibility in geometrical dimensions of surface microstructures opens a convenient way to tune the desired response to magnetic fields. The presented idea may find applications in impact mitigation or small-scale sorting machinery, e.g. for recycling.

关键词： magnetoactive elastomer image processing rebound mm-sized ball lamellar array magnetic field microstructured surface

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