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3rd International conference on Electronic Information Engineering and Computer Communication, EIECC 2023

作者： Cao, Yi Wei, Na Zhu, xiangping Ma, Jun Institute of Optics and Precision Mechanics University of Chinese Academy of Sciences State Key Laboratory of Transient Optics and Photonics Xi'an China Xi'An Institute of Atomic Precision Manufacturing Technology Co. Ltd. Xi'an China

ISBN: (纸本)9798350359961

In this study, a hardware platform based on the xilinx 7-series Field-Programmable Gate Array (FPGA) is employed. The design of a Static Storage Control Module and a Median Filtering Module is accomplished through programming in the Verilog hardware description language using Vivado software. The functionality of these modules is rigorously verified through logical validation using Modelsim software. The algorithm is applied to process low-light images captured by a custom EBCMOS image sensor. Comparative analysis with software testing platforms reveals a substantial improvement in image processing speed when utilizing the FPGA-based hardware resources. This improvement allows the processing of 1 pixel per clock cycle. As a result, this work lays the foundation for the design of lightweight, integrated data acquisition systems for the nextgeneration night vision helmet systems. © 2023 IEEE.

关键词： Algorithm Architecture EBCMOS Field-Programmable Gate Array (FPGA) Hardware Acceleration Hardware Description Language image processing

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

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

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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 Highway Traffic image Enhancement Algorithm Based on Improved GAN in Complex Weather Conditions

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION systems 2023年第8期24卷 8716-8726页

作者： Cheng, xin Zhou, Jingmei Song, Jiachun Zhao, xiangmo Changan Univ Sch Informat Engn Xian 710064 Peoples R China Traff Management Res Inst Minist Publ Secur Wuxi 214151 Peoples R China Changan Univ Sch Elect & Control Engn Xian 710064 Peoples R China Xian Technol Univ Sch Elect Informat Engn Xian 710021 Peoples R China

To address the problems of low resolution and blurred details in highway images caused by factors such as rain and fog, illumination interference, and nighttime lighting, this paper proposes a highway traffic image enhancement algorithm based on improved GAN in complex weather conditions. The attention mechanism and the multiscale feature fusion were combined to improve the generator network, which could effectively reduce noise while improving the attention of high-frequency region information. The improved PatchGAN in the discriminator used a local discrimination strategy to distinguish the generated image from the real image, and then the Nash equilibrium was achieved through the continuous interaction between the generator and the discriminator, to ensure the integrity and authenticity of the restored image. Compared with other image enhancement algorithms, using PSNR and SSIM as measurement indicators, the experimental results showed that the proposed algorithm's results were, respectively, 21.97% and 12.89% higher in nighttime enhancement, 26.16% and 12.75% higher in rain removal, and 26.56% and 12.1% higher in fog removal. The proposed algorithm can not only retain the image details and feature information, but also produce effective denoising, which increases the reliability of image-based traffic information processing and analysis.

关键词： image enhancement Meteorology Rain Generators Generative adversarial networks image color analysis Feature extraction Traffic image enhancement improved GAN attention mechanism multiscale feature

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A Review on Satellite image Segmentation Using Metaheuristic Optimization Techniques 1

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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...

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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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Real-time moving vehicle detection in satellite videos based on hole features and motion direction information

Real-time moving vehicle detection in satellite videos based...

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2024 International conference on Remote Sensing, Mapping, and Geographic Information systems, RSMG 2024

作者： Yuan, Jieran Song, Beibei Sun, Wenfang School of Information Engineering Chang’an University Xi’an710018 China School of Aerospace Science and Technology Xidian University Xi’an710068 China

ISBN: (纸本)9781510685826

Capturing motion vehicle information from satellite videos is crucial for real-time traffic monitoring and emergency response. However, vehicles in satellite videos are small in size, lack detailed textural features and are easily obscured by complex backgrounds. Traditional frame differencing and background subtraction methods often lead to a high number of false positives and false negatives, while deep learning methods struggle to meet real-time processing requirements. To strike a balance between detection performance and processing efficiency, a real-time vehicle detection method combining hole features extracted from frame differencing and motion direction estimation is proposed. Initially, a multiframe image accumulation (MIA) strategy is employed to enhance the visibility of vehicle targets and suppress background noise. Subsequently, the hole features of moving vehicles are extracted by differencing adjacent accumulated images, leading to the creation of a hole model for coarse detection of moving vehicles. Then, by estimating the motion direction of vehicles and enforcing direction consistency constraints, hole matching of neighboring vehicles in motion is achieved to improve the detection accuracy. Finally, a novel region extraction algorithm that integrates target hole features and motion direction information is designed to effectively suppress false positives generated by background noise. This method exhibits superior detection performance on the VISO benchmark dataset, achieving a recognition accuracy of 89.5% while meeting real-time processing requirements with an average processing time of only 0.04 seconds per frame, ensuring both detection performance and processing efficiency. © 2024 SPIE.

关键词： image segmentation

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Multimodal Fusion of Beidou Navigation Signal in Urban Navigation Services 6

Multimodal Fusion of Beidou Navigation Signal in Urban Navig...

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6th IEEE International conference on Power, Intelligent Computing and systems, ICPICS 2024

作者： Wang, Juan Zhang, Li He, Yifeng Xi'an Railway Vocational & Technical Institute Xi'an China China Academy of Space Technology Xi'an China

ISBN: (纸本)9798350374315

Navigation systems play an important role in urban navigation services, but traditional Beidou navigation signals still face problems such as low positioning accuracy and poor navigation stability in multimodal fusion. This article aimed to promote the improvement of positioning accuracy by exploring the multimodal fusion of Beidou navigation signals in urban navigation services, in order to better meet the needs of urban navigation services. Firstly, the signal data of Beidou Navigation Satellite was collected in the article;then, through steps such as data preprocessing, error correction, and data editing, the Beidou navigation signal data processing was achieved, improving the accuracy of navigation;afterwards, the Beidou navigation signal was combined with a one-dimensional structure, and multimodal fusion of the navigation signal was achieved through concatenation, resulting in the final classification result;finally, to verify the effectiveness of Beidou navigation using the algorithm proposed in this article for signal fusion in urban navigation services, it was compared with traditional algorithms. Through experiments, it was found that the average positioning accuracy of the Beidou navigation system for 15 positions after signal fusion using the algorithm proposed in this article could reach 94.25%, while the traditional algorithm only had 82.52%. The study has demonstrated the effectiveness of multimodal fusion of Beidou navigation signals, providing more ideas for promoting the construction of urban smart transportation and urban management. © 2024 IEEE.

关键词： Radio navigation

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

Automated Dengue Recognition and Analysis through Image Proc...

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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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