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

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

5th International Conference on Advances in Distributed Comp...

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

ISBN: (纸本)9789819718405

The proceedings contain 35 papers. The special focus in this conference is on Advances in Distributed Computing and Machine Learning. The topics include: Computer-Aided Bundle Branch Block Detection Using Symbolic Features of ECG Signal;block-Chain and Cloud-Based Tender Allocation System;reliability Assessment of IoT-Enabled systems Using Fault Trees and Bayesian Networks;resnet-50 Integrated with Attention Mechanism for Remote Sensing Classification;revolutionizing Data Annotation with Convergence of Deep Learning and Active Learning to Enhance Credibility on Twitter Datasets;statistical and Deep-Learning Approaches for Individual Carbon Footprint Calculation in India;intelligent Healthcare System Using Emerging Technologies: A Comprehensive Survey;Efficient Energy Management by Using SJF Scheduling in Wireless Sensor Network;cost Effective and Energy Efficient Drip Irrigation System for IoT Enabled Smart Agriculture;facial Detection and Recognition in Drone imagery Using FaceNet;valluvan: processing Name Board images to Enhance Communication for Native Tamil Speakers;Optimized VM Migration for Energy and Cost Reduction Using TSO Algorithm in Cloud Computing;A Study on the Mental Health Among Indian Population in the Post COVID-19 Pandemic Using Computational Intelligence;Cloud-Based Anomaly Detection for Broken Rail Track Using LSTM Autoencoders and Cross-modal Audio Analysis;Face Recognition Using CNN for Monitoring and Surveillance of Neurological Disorder Patients;a Novel Approach to Breast Cancer Histopathological image Classification Using Cross-colour Space Feature Fusion and Quantum–Classical Stack Ensemble Method;comparative Analysis of Deep Learning-Based Hybrid algorithms for Liver Disease Prediction;preface;performance Improvements of Covert Timing Channel Detection in the Era of Artificial Intelligence;sign Detection Using an N-Gram Language Model and MobileNet.

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Standardising Wound image Acquisition Through Edge AI 1

引用

9th International conference on Information and Communication Technologies for Ageing Well and e-Health, ICT4AWE 2023

作者： Vasconcelos, Maria João M. Sampaio, Ana Filipa Cardoso, Nuno Liberal, Marcos Alves, Paulo Marques, Raquel Salgado, Pedro Fraunhofer Portugal AICOS Porto Portugal Porto Portugal Centre for Interdisciplinary Research in Health Instituto de Ciências da Saúde UCP Porto Portugal F3M Information Systems S.A. Braga Portugal

ISBN: (数字)9783031627538

ISBN: (纸本)9783031627521

The high prevalence of chronic wounds, along with their impact on the patient’s life quality and healthcare systems, make them a relevant public health issue and have motivated the rise of digital health solutions to support wound monitoring. This paper presents a new framework that leverages deep learning models to automate wound image acquisition in real-time while guaranteeing focus and inclusion of an adequate periwound area. Considering an adhesive marker as a metric reference, a RetinaNet detection model is responsible for locating the wound and marker regions, further analysed by a post-processing module that validates if both structures are present and verifies if a periwound radius between 4 to 8 centimetres is included. The initial validation of this pipeline demonstrated that the developed algorithms exhibit a robust detection performance for varying acquisition conditions (translated into mAP@.75IOU values of 0.39 and 0.95 for wound and marker detection) and the deployability of the framework in an easily usable mobile application, without causing any performance hindrances. The proposed solution was then tested in a real environment by integrating the whole framework into a mobile application available in Android and iOS. During a two-month pilot study, healthcare professionals tested this application in their clinical practice. According to their feedback, the usage of the mobile application improved image quality and standardisation as the main advantages of the application, confirming the potential of the presented framework to streamline the image acquisition flow and make wound monitoring more reproducible. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Object detection

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image processing for automatic road inventory 15

Image processing for automatic road inventory

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15th International Scientific and Technical conference: Applied Mechanics and systems Dynamics, AMSD 2021

作者： Belim, S.V. Khiryanov, E.V. Kvashnina, P.A. Ostrinskaya, L.I. Omsk State Technical University 11 Mira ave Omsk644050 Russia Siberian State Automobile and Highway University Omsk Russia

Road image analysis is an important task for automatic road inventory. The determination geometric dimensions for the road and the identification road objects are subprocess of constructing a road digital image. In this article, two algorithms for solving different subtasks of automatic road image inventory are proposed. The first algorithm identifies road signs. A convolutional artificial neural network is used in this algorithm. The training set for the neural network is prepared. A computer experiment to determine the recognition effectiveness of road signs has been conducted. The second algorithm defines the edges of the pavement. The algorithm consists five stages. The edges of the road are modeled as straight lines. The result allows you to automatically determine the width of the road. © Published under licence by IOP Publishing Ltd.

关键词： Neural networks

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Identifying Adversarial Attacks in Crowdsourcing via Dense Subgraph Detection

Identifying Adversarial Attacks in Crowdsourcing via Dense S...

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Abdullah Karaaslanli Panagiotis A. Traganitis Aritra Konar Dept. of Electrical & Computer Engineering Michigan State University East Lansing MI USA Dept. of Electrical Engineering KU Leuven Leuven Belgium

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Crowdsourcing is becoming increasingly important for contemporary applications in machine learning and artificial intelligence. However, crowdsourcing systems may be susceptible to adversarial attacks where a subset of annotators deliberately provide erroneous responses. This paper introduces a novel algorithm for identifying adversarial attacks in crowdsourcing systems by recasting the problem as dense subgraph detection in bipartite graphs. In particular, we represent crowdsourced data as a weighted bipartite graph between workers and data points which are connected with edges whose weights are computed from annotators’ responses. The constructed bipartite graph is then analyzed with a sequential peeling algorithm to detect the dense subgraph that includes adversarial attacks. Compared to previous methods where only adversaries are detected, our proposed method can simultaneously identify adversarial annotators as well as affected data points. Preliminary results on real datasets showcase the potential of this novel approach.

关键词： Crowdsourcing Machine learning algorithms image edge detection Signal processing algorithms Machine learning Signal processing Acoustics Bipartite graph Speech processing

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A Framework for Mining and Applying Alarm Rules Based on LLM

A Framework for Mining and Applying Alarm Rules Based on LLM

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International conference on Vision, image and Signal processing (ICVISP)

作者： Guoqing Li Qian Zhang Haijun Su Mengyuan Zhu Mengqi Liu Haokai Zhang Development Center China Information Technology Designing & Consulting Institute Co. Ltd Beijing China

ISBN: (数字)9798331541460

ISBN: (纸本)9798331541477

Driven by 5G technology, network performance and management requirements are becoming increasingly sophisticated. However, massive and complex alarm information is also coming, posing severe challenges to network operation and maintenance. Accurately identifying alarm correlations and locating alarm root causes has become a core problem for operation and maintenance. In response to this challenge, this paper proposes a framework for mining and applying alarm rules based on large language models, which applies the text understanding capabilities of large language models to alarm processing and can mine uncommon alarm rules. Experimental results show that this framework significantly improves the accuracy and coverage of alarm rules, and improves the response speed and efficiency of network operation and maintenance.

关键词： Technological innovation Correlation Large language models Knowledge based systems Signal processing algorithms Signal processing Real-time systems Maintenance Communication networks Optimization

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Optimization of image Compression and Decompression Performance Based on Genetic Algorithm

Optimization of Image Compression and Decompression Performa...

引用

Interactive Intelligent systems and Techniques (IIST), International conference on

作者： Weishi Li Chun Yin Fujian Polytechnic of Information Technology Fuzhou China

ISBN: (数字)9798350374421

ISBN: (纸本)9798350374438

As one of the main carriers of information transmission and storage, image data need to be compressed in some application scenarios. Traditional image compression methods, although reducing the size of image files to a large extent, are still difficult to meet the performance requirements of massive, high-speed image data compression processing. In this paper, an image compression and decompression performance amelioration method based on genetic algorithm is proposed, aiming to ameliorate the efficiency and quality of image compression, while reducing the time and computational complexity of decompression. This paper first introduces the background and current status of image compression technology, and analyzes the progress of existing image compression research. Then, the basic principles of genetic algorithm and its application in image compression are introduced in detail. On this basis, an image compression algorithm based on genetic algorithm is proposed. The algorithm ameliorates the parameter settings in the image compression process through genetic algorithm to achieve efficient compression of images. At the same time, combined with the image decompression process, the decompression algorithm is ameliorated to reduce the consumption of computational resources. The research in this paper provides new ideas and methods for the performance amelioration of image compression and decompression technology, which has important theoretical and practical value.

关键词： image quality image coding Data compression Information processing image storage Intelligent systems Computational complexity Optimization Genetic algorithms Convergence

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Diabetic Retinopathy Lesion Detection Through Synergistic Analysis of Independent Components and Uncorrelated Discriminant Features 5

Diabetic Retinopathy Lesion Detection Through Synergistic An...

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

作者： Ameerjohn, Sirajudeen Manimaran, M. Saravanan, D. Devaraju, S. Kanna, G. Prabu Aanjankumar, S. School of Computing Science Engineering and Artificial Intelligence Vit Bhopal University Indore Bhopal Highway Madhyapradesh Sehore India School of Computing Science and Engineering Vit Bhopal University Indore Bhopal Highway Madhyapradesh Sehore India

ISBN: (纸本)9798350379945

The growing incidence of blindness, specifically linked to diabetic retinopathy (DR), highlights the need for expandable retina screening programs. The purpose of this work is to determine the distinction between normal and infected retina images by applying Independent Component Analysis (ICA). Although several processing methods help in DR detection, accuracy is still a problem. The accuracy of current techniques, such as multi-scale amplitude modulation and frequency modulation (AM-FM), to differentiate normal and diseased retinal images is restricted. The proposed method seeks to improve the identification of normal retina histories, haemorrhages, exudates, neovascularization, microaneurysms and blood vessel patterns. Optic Nerve Head (ONH) identification uses established parameters from AM and FM-based extraction of features to check illness traits, relying on edge data without disguising. Type 2 diabetes, which is typified by elevated blood sugar levels, causes damage to the arteries and diabetic retinopathy (DR), an important cause of permanent loss of vision that is frequently undiagnosed till severe. In the initial phases of diabetic retinopathy (DR), non-proliferative alterations that damage the walls of retina blood vessels develop to proliferation retinal degeneration, which is marked by aberrant formation of neovascularization. A lesion diagnosis is still difficult, despite its importance in the early identification of DR. For disease identification, the intricacy of retinal anatomy necessitates the use of many images and certain segmentation algorithms. Microaneurysms, haemorrhages, exudates and neovascularization are examples of lesions markers and identifying anomalies requires accurate segment. In conclusion, precise and affordable retinal scanning devices are necessary to deal with the increasing incidence of blindness, especially that caused by DR. To improve DR identification, this investigation suggests utilizing ICA and UDA. © 2024 IEEE.

关键词： image segmentation

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Design and Development of an Intelligent Wireless Pond/Lake Cleaning Robot

Design and Development of an Intelligent Wireless Pond/Lake ...

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Intelligent algorithms for Computational Intelligence systems (IACIS), International conference on

作者： Premkumar M Basavaraja Desai Bhoomika Mylar Chaithanya B K Monish G Department of Electrical and Electronics Engineering Dayananda Sagar College of Engineering Bangalore India Department of Electrical and Electronics Engineering Dayananda Sagar College of Engineering Bengaluru India

ISBN: (数字)9798350360660

ISBN: (纸本)9798350360677

This River pollution is a major environmental problem that has serious problems for human health, aquatic life, and the overall health of ecosystems. Floating garbage is a complicated problem to address, as it can be difficult to collect and remove from rivers and other waterways. Wireless river-cleaning robots can potentially be a valuable tool for addressing this problem. These robots can be equipped with a variety of sensors and actuators to detect and collect floating garbage, without the need for human efforts. This can help to reduce the cost and environmental impact of river cleaning operations, while also improving the effectiveness of these efforts. This paper aims to design and develop a wireless river-cleaning robot that is capable of autonomously detecting and collecting floating garbage. The robot will be designed with a variety of sensors, including cameras, ultrasonic sensors, and water quality sensors. image processing will be used to identify floating garbage, as well as to avoid obstacles and navigate the river safely. The robot will also be equipped with a collection system that can collect floating garbage and other debris. The robot will be controlled wirelessly using image processing and data will be stored on the website. This paper helps to reduce the water pollution on floating bodies and saves the lives of aquatic animals.

关键词： Wireless communication Wireless sensor networks image processing Surveillance Robot vision systems Cameras Cleaning Water pollution Rivers Sensors

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Study on Prostate image Segmentation Using Improved U-NET

Study on Prostate Image Segmentation Using Improved U-NET

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International conference on image, Vision and Intelligent systems, ICIVIS 2021

作者： Sun, Mengya Department of Mathematical Sciences University of Electronic Science and Technology of China Chengdu610000 China

ISBN: (纸本)9789811669620

Medical image plays a key role in the analysis and treatment of the patient’s condition in today’s medicine, but the clinical processing of medical image is still largely dependent on the subjective experience of doctors, and the process of manual extraction of information is time-consuming and labor-consuming. With the development of deep learning, it has become a trend to apply deep learning to medical image processing. However, due to the characteristics of medical images, many advanced segmentation algorithms fail to achieve good results in medical images. In addition, medical image data generally have the problem of insufficient or too small data sets, and there are few ways to obtain samples, mainly from some hospitals and medical institutions. Besides, the labeling work is not competent for ordinary people. So how to realize the automatic analysis and processing of medical images has always been a hot topic in the field of computer science. In this paper, we designed an experiment of prostate segmentation algorithm, built a model using a U-Net network that performs well in the field of medical image segmentation, and tried to improve its performance by combining a variety of structures, such as ASPP, ResNext, and attention module. The results show that our proposed model achieves better segmentation performance. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： image segmentation

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Realtime Scene Enhancement of Low Light image/Video

Realtime Scene Enhancement of Low Light Image/Video

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Intelligent Computing and Human-Computer Interaction (ICHCI), International conference on

作者： Huixia Zeng Ping Xiao School of Electronic & Information Engineering Guangzhou City University of Technology Guangzhou Guangdong China

ISBN: (数字)9798350368284

ISBN: (纸本)9798350368291

The quality of image and videos plays a vital role in case of real-time systems. images are captured without sufficient illumination, lead to low dynamic range and high propensity for generating high noise levels. The processing ideas and networks of several mainstream low-light image enhancement neural network algorithms such as DRBN, DSLR, ZeroDCE, EnlightenGAN, as well as the key technologies used are compared and analyzed. In this paper, the EnglightenGAN neural network is used as the core image enhancement algorithm, and NVIDIA AGX Xavier equipment and video stream processing technology are used to achieve the entire process of video acquisition, video frame encoding, image enhancement processing, frame decoding, and video stream display. It can adapt to most low-light and dark scenes which has a good enhancement effect on video streams collected in low-light scenes. The real-time performance and image enhancement effect of the system achieved good results, with FPS reaching 10, SSIM reaching 0.51753, and PSNR reaching 27.89973.

关键词： Runtime Neural networks Lighting Streaming media Real-time systems Stability analysis image enhancement Thermal stability Videos Noise level

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