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

共 12901 条记录，以下是171-180 订阅

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Optimization of deep learning algorithms in image recognition

Optimization of deep learning algorithms in image recognitio...

引用

2024 International conference on Computer Graphics, Artificial Intelligence, and Data processing, ICCAID 2024

作者： Liu, Yi Shaanxi University of Science and Technology Xi’an China

ISBN: (纸本)9781510689275

With the widespread application of deep learning technology in the field of image recognition, the main challenges it faces include insufficient recognition accuracy and high consumption of computing resources. This paper aims to improve the performance and efficiency of image recognition by analyzing and optimizing deep learning algorithms. The article first outlines the basic theory of deep learning and the commonly used models in image recognition, and then discusses in detail the optimization strategies such as data enhancement, network architecture innovation, transfer learning and hyperparameter adjustment. Through case analysis, the actual effect of these strategies in improving the accuracy and processing speed of facial recognition systems is demonstrated. The results show that the comprehensive application of these optimization methods can significantly improve the recognition performance and environmental adaptability of the model. The research conclusions support the future development of deep learning in image recognition technology, and will continue to optimize algorithms and improve application efficiency. © 2025 SPIE.

关键词： data enhancement deep learning image recognition network architecture

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Fuzzy Morphological processing Algorithm of Blood image 25

Fuzzy Morphological Processing Algorithm of Blood Image

引用

25th IEEE International conference of Young Professionals in Electron Devices and Materials, EDM 2024

作者： Turgunov, Abrorjon M. Narzullaev, Temur N. Matyakubov, Marks Ya. Payzieva, Munira T. Kamolova, Sevara J. Rakhmonova, Nilufar N. Department of Algorithms and Mathematical Modeling Tashkent University of Information Technologies named after Muhammad al-Khwarizmi Tashkent Uzbekistan Tashkent University of Information Technologies named after Muhammad al-Khwarizmi Tashkent Uzbekistan

ISBN: (纸本)9798350389234

This article, a morphological image processing algorithm is developed for early detection of viral hepatitis from microscopic blood images. It is mentioned that it can be used to accurately determine each boundary detail in the fuzzy morphological processing of the gray image of the blood image. Boundary detection leads to higher recognition results in neural network training. Compared to the work of a number of scholars, our method offers such performance, but it offers improved dynamics and flexibility in the formulation of linguistic boundary criteria, which can be a leading factor in the design of image processing systems with dynamic and flexible rules, such as Type 2 allows fuzzy rules, which offers an interesting alternative to currently common deep learning applications. The validated experimental results show the reliability of the modified method compared to the Chaira method. Mean, entropy and standard deviation were calculated. It turned out to be higher than the values of the Chaira method. © 2024 IEEE.

关键词： Neural networks

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2024 10th International conference on Signal processing and Intelligent systems, ICSPIS 2024

2024 10th International Conference on Signal Processing and ...

引用

10th International conference on Signal processing and Intelligent systems, ICSPIS 2024

ISBN: (纸本)9798331532543

The proceedings contain 46 papers. The topics discussed include: continuous and non-contact extraction of respiratory signal and rate from thermal video;phonocardiogram signal denoising using dictionary learning;presenting a method for gluing error detection using image processing;alleviating undesired distance effect in spatio-temporal based video anomaly detection;real-time audio analysis for detection of apnea intervals in health-care system;classification of electric motors faults using Fourier-based features and self-organizing maps;validation of CMA simulation in analyzing the impact of electromagnetic waves on drone electronic boards;a hybrid approach for sentiment analysis of Arabic tweets;and line segmentation in Persian texts in double columns using hierarchical clustering algorithms.

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A Review on Satellite image Segmentation Using Metaheuristic Optimization Techniques 1

引用

5th International conference on Advances in Distributed Computing and Machine Learning, ICADCML 2024

作者： Bandikolla, Lakshmi Md Khairuzzaman, Abdul Kayom School of Electronics Engineering VIT-AP University Amaravati Andhra Pradesh Vijayawada522237 India

ISBN: (数字)9789819718412

ISBN: (纸本)9789819718405

image segmentation is essential in digital image processing applications. Multilevel thresholding is a popular technique for image segmentation. An image can be divide into multiple classes. In this paper, the image quality metrics such as peak signal-to-noise ratio (PSNR), mean squared error (MSE), mean structural similarity (SSIM), feature similarity (FSIM) are compared using metaheuristic algorithms. The CPU time of different metaheuristics is also compared to determine the time complexity of the techniques. The Otsu’s method, Kapur’s entropy, Tsallis entropy, and Masi entropy are used as objective functions. Some of the metaheuristic algorithms, adaptive Cuckoo search algorithm (ACS), chaotic coronavirus optimization algorithm (COVIDOA), dynamic quantum-inspired genetic algorithm (DQGA), a modified artificial bee colony algorithm (MABC), dynamic Harris hawks with mutation mechanism (DHHO/M), and particle swarm optimization algorithms (PSO) are compared. As per the comparison of the quality metrics, DHHO/M with Otsu was more efficient in average PSNR. CS with Tsallis provides best value in average MSE, ACS with Otsu gives best results in average SSIM, ACS with Tsallis is more efficient in average FSIM, and MABC with Kapur’s combination was more efficient in average CPU time. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： image segmentation

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Automated Defect Detection and Segregation in Dyed Fabrics Using image processing Techniques 5

Automated Defect Detection and Segregation in Dyed Fabrics U...

引用

5th International conference on Electronics and Sustainable Communication systems, ICESC 2024

作者： Prabha, K.R. Aathavan, V. Harshit, K. Indhumathi, K. Sri Ramakrishna Engineering College Department of Ece Coimbatore India

ISBN: (纸本)9798350379945

This study addresses the critical need for efficient quality control in textile manufacturing, aiming to enhance the production of high-quality fabric to meet consumer demands and industry benchmarks. Despite the meticulous cleaning and dyeing processes involved in fabric production, there remains a challenge in identifying and segregating defective sections, which can potentially compromise the overall quality of the fabric. To overcome this challenge, this research study proposes an innovative solution: Automated Quality Control in Textile Manufacturing, utilizing advanced image processing techniques. The proposed system employs high-resolution cameras and image processing algorithms to analyze the fabric's surface in real-time. By leveraging predefined criteria and machine learning models, the system can accurately detect and isolate flawed sections within the fabric. These criteria encompass a range of defects, including stains, discolorations, or irregular patterns that deviate from established quality standards. This approach improves quality control in textile manufacturing, providing manufacturers with a reliable and efficient means of identifying and addressing defects in fabric production. By automating the quality control process, manufacturers can enhance productivity, reduce waste, and ultimately deliver higher-quality products to consumers. © 2024 IEEE.

关键词： Textile industry

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Invisible image Watermarks Are Provably Removable Using Generative AI 38

Invisible Image Watermarks Are Provably Removable Using Gene...

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38th conference on Neural Information processing systems, NeurIPS 2024

作者： Zhao, Xuandong Zhang, Kexun Su, Zihao Vasan, Saastha Grishchenko, Ilya Kruegel, Christopher Vigna, Giovanni Wang, Yu-Xiang Li, Lei UC Berkeley United States Carnegie Mellon University United States UC Santa Barbara United States UC San Diego United States

Invisible watermarks safeguard images' copyrights by embedding hidden messages only detectable by owners. They also prevent people from misusing images, especially those generated by AI models. We propose a family of regeneration attacks to remove these invisible watermarks. The proposed attack method first adds random noise to an image to destroy the watermark and then reconstructs the image. This approach is flexible and can be instantiated with many existing image-denoising algorithms and pre-trained generative models such as diffusion models. Through formal proofs and extensive empirical evaluations, we demonstrate that pixel-level invisible watermarks are vulnerable to this regeneration attack. Our results reveal that, across four different pixel-level watermarking schemes, the proposed method consistently achieves superior performance compared to existing attack techniques, with lower detection rates and higher image quality. However, watermarks that keep the image semantically similar can be an alternative defense against our attacks. Our finding underscores the need for a shift in research/industry emphasis from invisible watermarks to semantic-preserving watermarks. Code is available at https://***/XuandongZhao/WatermarkAttacker. © 2024 Neural information processing systems foundation. All rights reserved.

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Improving Robustness of Satellite image processing Using Principal Component Analysis for Explainability

Improving Robustness of Satellite Image Processing Using Pri...

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19th International conference on Software Technologies, ICSOFT 2024

作者： Witteck, Ulrike Stambke, Jan Grießbach, Denis Herber, Paula Berlin-Adlershof Germany Embedded Systems Group University of Munster Germany

ISBN: (纸本)9789897587061

Finding test-cases that cause mission-critical behavior is crucial to increase the robustness of satellite on-board image processing. Using genetic algorithms, we are able to automatically search for test cases that provoke such mission-critical behavior in a large input domain. However, since genetic algorithms generate new test cases using random mutations and crossovers in each generation, they do not provide an explanation why certain test cases are chosen. In this paper, we present an approach to increase the explainability of genetic test generation algorithms using principal component analysis together with visualizations of its results. The analysis gives deep insights into both the system under test and the test generation. With that, the robustness can be significantly increased because we 1) better understand the system under test as well as the selection of certain test cases and 2) can compare the generated explanations with the expectations of domain experts to identify cases with unexpected behavior to identify errors in the implementation. We demonstrate the applicability of our approach with a satellite on-board image processing application. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Satellite imagery

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Automated Dengue Recognition and Analysis through image processing and Machine Learning 2

Automated Dengue Recognition and Analysis through Image Proc...

引用

2nd IEEE International conference on Integrated Circuits and Communication systems, ICICACS 2024

作者： Patil, Preethi N Usha, J. Dharani, Andhe RV College of Engineering Department of MCA Bangalore India

ISBN: (纸本)9798350317558

Detecting dengue fever using image processing techniques typically involves the analysis of medical images such as blood smears or tissue samples. Dengue is a viral disease transmitted by Aedes mosquitoes, and its diagnosis primarily relies on clinical symptoms, serological tests, and molecular assays. image processing can be used as a complementary tool to assist and identify the dengue virus or related disparities in the body. Dengue fever, alternatively known as break-bone fever, is a lifethreatening arboviral disease caused by the DENV virus. It is a major global health concern with an increasing incidence worldwide. The existing clinical methods for diagnosing dengue are manual, requiring significant time and labor. The present work aims to develop an automated system using machine learning and digital image analysis to detect dengue infection from blood smear images. The custom algorithms are designed to extract platelets, red blood cells, and white blood cells, and train a feature vector. By automating the analysis of blood smear images, aids healthcare professionals detect cases of dengue more efficiently, allowing for early treatment and potentially saving lives. However, it is crucial to address data quality, algorithm selection, validation, interpretability, and ethical considerations throughout the development process. © 2024 IEEE.

关键词： CIELab space Dengue DENV edge detection histogram thresholding Hough transform pixel intensity

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Patch based image processing for complex environment characterization 6

Patch based image processing for complex environment charact...

引用

6th IEEE International conference on image processing, Applications and systems, IPAS 2025

作者： Menier, Corentin Meurie, Cyril Guillemaille, Timothee Ruichek, Yassine Marais, Juliette Univ Gustave Eiffel COSYS-LEOST Villeneuve d'AscqF-59650 France UTBM BelfortF-90010 France

ISBN: (纸本)9798331506520

Environment analysis is a critical part of autonomous vehicle for transport applications and for passenger safety. The solutions demonstrating the greatest robustness have been integrating multiple sensors used for redundancy and refinement purposes. Vision applications have proven to offer a high degree of flexibility and performance. One particular instance of this is higlighted in vehicle localisation, which predominantly relies on GNSS-based systems for positioning calculation using propagation time measurements. However, this signal may be degraded through the environment around the vehicle, worst case being urban canyons leading to Non Line Of Sight(NLOS) scenarios or multipaths issues due to reflecting obstacles. Previous work have shown vision-based algorithms can be used to mitigate these effects. One widely studied approach relies on the segmentation of an acquired wide-angle image installed on the roof of the vehicle and oriented toward the *** the sky processing module is binary, the pipeline lack any way to express its uncertainty when applying weighting policies to the detected satellite state, which can be detrimental to the resulting positioning. In this paper, we propose a novel way of analysing wide-angle camera images, also known as fisheye images, dividing the image into patches to output the corresponding situation of each region of interest. Additionally we propose a new class to the previous sky versus non-sky segmentation, designated as mixed class and designed to serve as a fuzzy answer by the deep learning model to improve confidence to other scenarios as well as allow for new analysis policies of satellites signals. The data-driven algorithm is designed and tested on a publicly available dataset, composed of a large number of finely labelled images provided by ISAE-SUPAERO reaching a 94% accuracy. © 2025 IEEE.

关键词： image segmentation

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Analysis of Decision Tree and Random Forest algorithms for Nutrient Deficiency Classification in Citrus Leaves through image processing 17

Analysis of Decision Tree and Random Forest Algorithms for N...

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17th International conference on Telecommunication systems, Services, and Applications, TSSA 2023

作者： Kamelia, Lia Nurmalasari, Rin Rin UIN Sunan Gunung Djati Bandung Electrical Engineering Department Bandung Indonesia

ISBN: (纸本)9798350309164

The research presents a hybrid approach to identify and categorise nutritional deficiency syndrome in citrus leaves using image processing and machine learning. The method includes processing images, segmenting images and extracting color-Texture information. The Decision Tree and the Random Forest algorithms use various statistical measures for classification, including mean, skewness, variance, entropy, maximum probability, angular second moment, contrast, correlation, dissimilarity, energy, and homogeneity. The test results show a range of performance levels, with an accuracy of 80.28% being attained by utilising an 80:20 data ratio in the Decision Tree approach. The Random Forest method's highest accuracy, 91.25%, was reached using an 80:20 ratio. In conclusion, the accuracy of the Random Forest approach regularly beats that of the Decision Tree method. © 2023 IEEE.

关键词： Decision trees

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