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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371-380 订阅

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The astronomical object recognition and its near-zero motion detection in series of images by in situ modeling 29

The astronomical object recognition and its near-zero motion...

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29th International conference on systems, Signals and image processing (IWSSIP)

作者： Khlamov, Sergii Savanevych, Vadym Tabakova, Iryna Trunova, Tetiana Kharkiv Natl Univ Radio Elect Kharkiv Kharkiv Ukraine Kharkiv Natl Univ Radio Elect Kharkiv Dept Syst Engn Kharkiv Ukraine Kharkiv Natl Univ Radio Elect Dept Media Syst & technol Kharkiv Ukraine

ISBN: (数字)9781665495783

ISBN: (纸本)9781665495783

In this work we have presented the approach for the astronomical object recognition and detection of its near-zero motion in the series of images and compressed videos from the "live" (online) data stream using the developed method of in situ modeling. We have described all required preconditions and constants for the method of in situ modeling. The substitutional method implemented on a maximum likelihood criterion and the method implemented on the Fisher f-criterion were selected as the detection algorithms for the near-zero motion of objects for the current research. We have analyzed the several quality indicators of detection of the objects near-zero motion by applying the statistical imitation modeling techniques and using the conditional probability of the true detection.

关键词： in situ modeling OLS-evaluation F-test maximum likelihood criterion hypothesis image recognition motion detection series of images machine vision

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Smart Healthcare: A Logical Development of Medical image processing Based Sleep Apnea Disease Detection Using Elevated Neural Classification Principle

Smart Healthcare: A Logical Development of Medical Image Pro...

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

作者： Jagadeesh, P. Radhamani, V. Ravi, T. Charulatha, G. Shahare, D.Y. Aljawawdeh, Hamzeh J. Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering college Department of Electronics and Communication Engineering Avadi Chennai India Rajalakshmi Engineering College Department of Ece Chennai India Sathyabama Institute of Science and Technology Department of Ece Chennai India Selvam College of Technology Department of Biomedical Engineering Namakkal India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Zarqa University Faculty of information technology Department of Software Engineering Zarqa Jordan University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331543617

Entirely common sleep disorder is the dangerous wakefulness (Apnea) of breathing during snoring. In this study, we explore the possibility of how medical image processing can be useful and accurate method for sleep disordered breathing detection. The new approach named as Sleep Apnea Analyzing Strategy (SAAS) is firstly tested with the help of existing method namely image base Sleep Apnea Analyzer (ISAA) that use less number or data to be processed and hence running on portable device delivering results much faster than other available methods, both being cross-validated against each other. Sleep Apnea image-based Detector using Ephysiology - A new Algorithm (SAAS) We rely on sophisticated image processing techniques to extract relevant characteristics from medical images (such as x-rays, CT scans or MRIs) which are indicative of patterns typically associated with sleep apnea. The proposed methodology includes image preprocessing to improve the quality of images, feature extraction for quantification and characterization of all relevant information during OSA trials in GUI interface through well-known classification algorithms to classify patients with at least 20 de-saturations without sleep apnea. Our research aims to help in the creation of automatic diagnostic methods that will lead to early detection and treatment for sleep apnea. Our objective with this work is to provide a fair evaluation of the proposed technique in terms of sensitivity, specificity and accuracy through using a large dataset. © 2024 IEEE.

关键词： Sleep research

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Algorithm for finding reference brightness correction coefficients 3

Algorithm for finding reference brightness correction coeffi...

引用

3rd International conference on Optics, Computer Applications, and Materials Science, CMSD 2023

作者： Abdullayeva, Ozoda Beknazarova, Saida Namangan Engeneering-Construction Institute Namangan Uzbekistan Tashkent University of Information Technologies Named after Muhammad Al-Khwarizmi Tashkent Uzbekistan

ISBN: (纸本)9781510674486

This article presents an algorithm for determining reference brightness correction coefficients to improve image quality. The algorithm utilizes a combination of statistical analysis and image processing techniques to identify and correct brightness discrepancies in digital images. By establishing a robust and efficient method for calculating these coefficients, the algorithm aims to enhance the overall visual fidelity of images across various applications, such as photography, medical imaging, and remote sensing. The proposed algorithm demonstrates its effectiveness through experimental results and comparisons with existing methods, highlighting its potential for practical implementation in image processing workflows. In the world, the filtering of digital images by the convolution method with a pulse characteristic in the spectral region scientific research is being conducted to improve the quality level of digital television images, methods for modeling filtration processes and highly efficient control systems in a number of priority areas, including: on the formation of mathematical models of filtration processes, improving the methods of wavelet, Fourier, Haar, Walsh-Hadamard, Karhunen-Loev in increasing the clarity and brightness of images based on linear and nonlinear differential equations;creation of methods for eliminating additive, pulsed and adaptive-Gaussian types of noise in images using additive and adaptive filtering;methods of algorithms and software for introducing intra-frame and interframe image transformations;methods of adaptive brightness system control using the Chebyshev matrix series;methods of gradient, static and Laplace methods for image segmentation and dividing it into contours;formation of criteria and conditions for evaluating image quality. Conducting scientific research in the above research areas confirms the relevance of the topic of this article. © 2024 SPIE. All rights reserved.

关键词： image enhancement

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Design and Development of DenseNet Transfer Learning Model for image Classification 4

Design and Development of DenseNet Transfer Learning Model f...

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4th International conference on Technological Advancements in Computational Sciences, ICTACS 2024

作者： Attri, Vikas Kapoor, Ankush Singh, Simranjeet Sharma, Payal University School of Computing Rayat Bahra University Mohali India University Institute of Computing Chandigarh University Mohali India

ISBN: (纸本)9798350387490

Integrating deep learning with computer vision technologies and analysing their application efficiency is the main emphasis of this research. Building hierarchical neural networks, a key component of deep learning, allows for end-to-end feature learning and semantic image processing, marking a significant advancement in the field. When training deep learning algorithms, the successes in computer vision give a solid foundation. Combining these two areas has produced cutting-edge computer vision systems that outperform the state-of-the-art in object detection and machine vision image classification. This research uses a combination of everyday image classification situations to examine the deep neural network models' better performance, draw attention to their shortcomings in interpretability and generalizability, and suggest ways to enhance them in the future. In general, advancements in computer vision will be propelled by the expanding use of deep learning and its efficient integration with large amounts of visual data. This trend will pave the way for intelligent machine vision systems. After briefly introducing deep learning, this paper proposed the most recent model used for deep learning picture classification that generates an accuracy of 96.3 %. Lastly, the accuracy of our deep learning model compared with other models used in the literature for the most challenging datasets, CIFAR-10, imageNet, and CIFAR-100. © 2024 IEEE.

关键词： Machine vision

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Pre-processing algorithms for Accurate Analysis of an image: A Review 5

Pre-Processing Algorithms for Accurate Analysis of an Image:...

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5th International conference on Smart systems and Inventive Technology, ICSSIT 2023

作者： Satuluri, Yasaswini Prabha, K. Venkata Ratna Polavarapu, Jairam Swamy Battina, Harsha Vardhan Kota, Venu Ramesh, P. V R Siddhartha Engineering College Dept. of Electronics and Communication Engineering Vijayawada520007 India Dr. SandNS Institute of Dental Sciences Dept. of Conservative Dentistry and Endodontics AP Chinoutpalli India

ISBN: (纸本)9781665474672

Real-world databases nowadays are particularly vulnerable to noisy, missing and inconsistent data due to their large size (often several terabytes or so more), as well as the potential that they come from multiple and diverse sources. Poor mining findings will result from low-quality data. To produce the appropriate image for a better analysis, images from the relevant dataset are pre-processed using a variety of currently available approaches. In this work, some of the numerous pre-processing methods are discussed and it is observed that filtering techniques like Gabor filter are more popular than other state-of-the-art techniques like interpolation, kernels etc. © 2023 IEEE.

关键词： Interpolation

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Harnessing AI and Imaging Innovations for Enhanced Detection and Diagnosis of Age-Related Macular Degeneration: An In-Depth Review 4

Harnessing AI and Imaging Innovations for Enhanced Detection...

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4th International conference on Artificial Intelligence and Signal processing

作者： Swaroop, Sana Rao, Manda Rama Narasinga GITAM Univ Dept Comp Sci & Engn Visakhapatnam Andhra Pradesh India

ISBN: (纸本)9798350350661;9798350350654

Age-related macular degeneration (AMD) is a leading cause of irreversible vision impairment among the elderly population worldwide, affecting over 196 million people globally. This study delves into innovative approaches for improving the detection and management of AMD through advanced technological solutions. To investigate the impact of ultraviolet (UV) and blue light exposure on AMD, the research analyzes their contribution to oxidative stress and retinal damage and evaluates potential protective measures or interventions to mitigate these effects. By leveraging state-of-the-art machine learning algorithms and advanced image processing techniques, the research aims to enhance the precision and efficiency of AMD diagnosis. The statistical burden of AMD underscores its significant impact on global health, with projections indicating a rising prevalence due to aging populations, lifestyle factors, and increasing digital screen use among younger generations. Effective management hinges on early detection and accurate monitoring of AMD biomarkers, which these methodologies seek to facilitate. Experimental evaluations demonstrate promising outcomes in diagnostic accuracy and scalability, highlighting the potential for widespread adoption in clinical practice. Furthermore, these advancements contribute to broader efforts in global eye health by offering scalable, AI-driven solutions that can improve patient outcomes and streamline healthcare workflows. By addressing the complexities of AMD diagnosis, this research supports healthcare providers in delivering timely interventions and personalized care, ultimately reducing the burden of AMD-related vision loss on individuals and healthcare systems.

关键词： AMD Vision Impairment image processing techniques AMD Biomarkers Diagnostic Accuracy AI-driven Solutions Healthcare Workflows Digitalization

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Approach to controlling laser processes with the help of AI 14

Approach to controlling laser processes with the help of AI

引用

2025 conference on High Power Laser Materials processing

作者： Wetzig, Andreas Szep, Jeno Ullmann, Linda Dittrich, Dirk Meyer, Julia Schlosser, Michael Rauscher, Peter Pudell, Stefan Schoeps, Patrick Vogelsang, Stefan Sonntag, Frank Fraunhofer IWS Winterbergstr 28 D-01277 Dresden Germany Fraunhofer CMA 5700 Rivertech CourtSuite 210 Riverdale MD 20737 USA

ISBN: (纸本)9781510684607;9781510684614

In the modern industrial context, laser processes, such as laser cutting and laser welding, are predominantly monitored and partially controlled in specific areas, such as process abort scenarios or axis actuator movements. Industrial driven interfaces like OPC UA or proprietary bus interface recently allow data acquisition from those control units within certain limits. This data can be augmented with highly accurate scientific data sources. In our proposed setup this is achieved by integrating laser acoustic sensors along with high speed cameras operating in visual and thermal spectrum. The variety of available data sources offers a significant potential for further processing and analysis via artificial intelligence (AI), contributing to deeper process understanding and further development of enhanced control algorithms of laser material machining processes. A post-mortem annotation with quality characteristics such as dross formation, surface roughness, welding depth, porosity, crater formation, etc. deliver all premises to develop and train AI based control models. To link all data sources and annotations a common time management and time normal is required. Its time resolution depends on the fastest cycle time governing a control answer, typically executed in the range of sub milliseconds. A time scale smaller than standard AI algorithms typically deliver complex inference results. Our paper presents an approach to close the time gap by introducing a smart control platform capable of capturing and preprocessing data in real time by utilizing hardware accelerated acquisition algorithms and time management (FPGA-MPSoC). The solution was implemented, transferred to a state of the art welding and cutting setup, and successfully tested. A foundation for an AI controlled laser machining process is set.

关键词： real time control image and audio processing hardware acceleration AI preprocessing laser welding laser cutting FPGA-MPSoC PTP

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image QUANTIZATION TOWARDS DATA REDUCTION: ROBUSTNESS ANALYSIS FOR SLAM METHODS ON EMBEDDED PLATFORMS 29

IMAGE QUANTIZATION TOWARDS DATA REDUCTION: ROBUSTNESS ANALYS...

引用

IEEE International conference on image processing (ICIP)

作者： Picard, Quentin Chevobbe, Stephane Darouich, Mehdi Didier, Jean-Yves CEA LIST F-91191 Gif Sur Yvette France Univ Paris Saclay Univ Evry IBISC F-91025 Evry France

ISBN: (数字)9781665496209

ISBN: (纸本)9781665496209

Embedded simultaneous localization and mapping (SLAM) aims at providing real-time performances with restrictive hardware resources of advanced perception functions. Localization methods based on visible cameras include image processing functions that require frame memory management. This work reduces the dynamic range of input frame and evaluates the accuracy and robustness of real-time SLAM algorithms with quantified frames. We show that the input data can be reduced up to 62% and 75% while maintaining a similar trajectory error lower than 0.15m compared to full precision input images.

关键词： SLAM localization image processing embedded systems real-time frame reduction

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Comparative Study of Various Machine Learning algorithms for American Sign Language Recognition 1

引用

6th International conference on Soft Computing and Signal processing, ICSCSP 2023

作者： Malhan, Prabhat Gusain, Surbhi Raturi, Shashank Singh, Prabhdeep CS/IT Graphic Era University Deemed to be University Uttarakhand Dehradun248002 India

ISBN: (数字)9789819986286

ISBN: (纸本)9789819986279

This research paper presents a comparative study on various machine learning algorithms for sign language detection. The objective of this study is to find the sign language identification method that is most accurate and effective for usage in real-time applications. The Residual Network (ResNet), Artificial Neural Network (ANN), Convolutional Neural Networks (CNNs), VGG16, and MobileNet are five well-known machine learning methods whose performance is compared. The dataset utilized in this study comprises camera-captured sign language motions made by diverse people. We assess each algorithm’s performance using a variety of parameters, including accuracy. According to our findings, VGG16 performs better than the other four algorithms, with an accuracy rate of 99%. Therefore, we conclude that VGG16 is the most suitable algorithm for sign language detection, which can be used in real-time applications for the deaf and hearing-impaired community. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： image classification

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Computational Assessment Model for Blind Medical image Watermarking with Deep Learning 6th

Computational Assessment Model for Blind Medical Image Water...

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6th conference on Computational Methods in systems and Software (CoMeSySo)

作者： Chacko, Anusha Chacko, Shanty Karunya Inst Technol & Sci Dept Elect & Commun Engn Coimbatore Tamil Nadu India Vimal Jyothi Engn Coll Dept Elect & Commun Engn Kannur Kerala India Karunya Inst Technol & Sci Dept Elect & Elect Engn Coimbatore Tamil Nadu India

ISBN: (纸本)9783031214370;9783031214387

Blind watermarking is identified as one of the effective method of data hiding in image processing;however, existing literatures shows an adoption of sophisticated technique considering specific attacks. However, a potential gap is found where there is no report of resiliency of using blind watermarking towards resisting lethal threats. Therefore, this manuscript contributes towards offering a computational assessment model by constructing a lethal blind watermarking attacker model where a discrete orthogonal moments are extracted followed by dithering. The model is assessed on multiple modalities of standard medical image dataset as well as deep learning models. The outcome shows presented model accomplishes more than 45% of performance degradation from accuracy perspective. This outcome will offer clear guidelines of using deep learning models considering different medical image modalities to achieve better watermarking performance.

关键词： Blind watermarking Data hiding Attack Deep learning Modalities Accuracy Medical image

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