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

共 13664 条记录，以下是1401-1410 订阅

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Visionary AI Enhancing Diabetic Retinopathy Detection through image processing

Visionary AI Enhancing Diabetic Retinopathy Detection throug...

引用

Intelligent Cyber Physical systems and Internet of Things (ICoICI), International conference on

作者： P Vinayagam S Santhiya S Naveen Kumar V Dhanush Kumar Department of ECE Saveetha Engineering College Tamilnadu India

ISBN: (数字)9798331540661

ISBN: (纸本)9798331540678

The development of Diabetic Retinopathy (DR) necessitates comprehensive screening techniques to avoid visual loss. Existing manual screening techniques are subjective and prone to errors, resulting in delayed diagnosis and poor patient outcomes. In response, the paper introduces a Visionary AI system that transforms DR detection via image processing methods and Convolutional Neural Networks (CNNs). The proposed system decreases the pressure on healthcare personnel by automating analyses and assuring consistency and accuracy. The proposed system outperforms existing systems, achieving 95% accuracy, 92% sensitivity, 96% specificity, and an AUC-ROC value of 0.97. Furthermore, the proposed system demonstrates scalability and adaptability, indicating a larger application in a variety of healthcare contexts. Future research aims to expand the proposed system's capabilities and incorporate it seamlessly into healthcare workflows, with a focus on its ability to greatly improve DR detection and patient outcomes.

关键词： Diabetic retinopathy Visualization Accuracy Sensitivity Scalability Medical services Transforms Retina Prediction algorithms Convolutional neural networks

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Design of an FPGA-based image Defogging Algorithm Using Dark Channel Prior

Design of an FPGA-based Image Defogging Algorithm Using Dark...

引用

Electronic Information Engineering and Computer Science (EIECS), 2021 International conference on

作者： Tingting Zhang Chengdu University of Information Technology School of Communication Engineering Chengdu Sichuan China

ISBN: (数字)9798331531409

ISBN: (纸本)9798331531416

In recent years, with the rapid advancement of digital image processing technology, image dehazing has become a focal point in both scientific research and practical applications. This paper presents a study on a dehazing algorithm based on the dark channel prior and its optimization for FPGA implementation. To improve image refinement and reduce algorithm complexity, the filter window size for calculating the dark channel is simplified to 3×3. When estimating the global atmospheric light intensity (A), the brightest pixel in the dark channel image is selected as the A value. Based on this, the transmission map is computed, and a guided filter is introduced to smooth the image. Finally, FPGA is utilized to accelerate the algorithm, and a low-pass filter is employed instead of the guided filter to further reduce complexity and enhance processing efficiency, meeting the real-time dehazing requirements for high-resolution images.

关键词： Computer science Digital images Low-pass filters Channel estimation Filtering algorithms Information filters Real-time systems Complexity theory Field programmable gate arrays Optimization

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Vehicle image Anomaly Detection Based on Improved K-Means Algorithm and High-Resolution Camera

Vehicle Image Anomaly Detection Based on Improved K-Means Al...

引用

Machine Learning and Autonomous systems (ICMLAS), International conference on

作者： Xiaoqin Yang Syazwina Alias Hui Wang FoEBEIT SEGi University Kota Damansara Selangor Malaysia College of Computer and Communication Engineering Nanjing Tech University Pujiang Institute Nanjing Jiangsu China

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

This study proposes a vehicle image anomaly detection framework based on improved K-means algorithm and high-resolution camera, which provides a new idea for road safety construction from the image processing level. The main innovation of the study contains 4 aspects: 1. A new vehicle target extraction algorithm based on deep learning is proposed. The algorithm uses the YOLOv5s network structure and combines the SimAM attention mechanism to optimize the model while improving the detection accuracy of vehicle targets. 2. A visual-based traffic flow clustering analysis is performed. This analysis achieved effective clustering of traffic flow through deep HOG feature extraction and parallel K-means clustering algorithm. 3. An optimized license plate detection and recognition algorithm based on YOLOv5 is proposed. The algorithm improves the accuracy of license plate recognition by adding the key point detectors. 4. The traditional K-means algorithm is optimized. The optimization idea is mainly to combine quantum computing and space-time theory. This optimization improves the convergence speed and clustering effect of the algorithm. The results of comparative experiments show that the improved K-means algorithm converges faster in traffic flow clustering analysis, and its license plate recognition accuracy is significantly better than traditional algorithms (such as k-means and FCM).

关键词： YOLO Technological innovation Machine learning algorithms Accuracy Clustering algorithms Cameras Road safety License plate recognition Anomaly detection Optimization

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Computer Vision Based Hybrid Classroom Attention Monitoring

Computer Vision Based Hybrid Classroom Attention Monitoring

引用

Information Technology, Electronics and Intelligent Communication systems (ICITEICS), IEEE International conference on

作者： Saniya Rawat Malivia Rodrigues Prateeksha Sheregar Kalpita Ajinkya Wagaskar Amiya Kumar Tripathy Department of Computer Engineering Don Bosco Institute of Technology Mumbai India

ISBN: (数字)9798350382693

ISBN: (纸本)9798350382709

This research presents a novel computer vision-based attention monitoring system designed for both online and offline contexts. Leveraging advanced image processing and machine learning algorithms, the system analyzes human gaze patterns, eye movements, and facial expressions to accurately gauge attention levels. In online scenarios, the system employs real-time webcam-based gaze tracking and facial recognition to provide immediate insights into user engagement during activities like video conferencing and virtual meetings. For offline analysis, recorded video footage is retrospectively examined, facilitating applications in education, workplace productivity, and user experience assessments. Privacy considerations are addressed through the implementation of privacy-preserving techniques. Experimental results demonstrate the system's efficacy in monitoring attention dynamics across diverse settings, contributing to a deeper understanding of human attention in various domains.

关键词： image processing Face recognition Education Gaze tracking Web conferencing Streaming media Real-time systems

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Comparative Analysis of Machine Learning based Intrusion Detection systems

Comparative Analysis of Machine Learning based Intrusion Det...

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International conference on image Information processing

作者： Siddharth Pansare Arun Malik Isha Batra Computer Science and Engineering Lovely Professional University Phagwara India

ISBN: (数字)9798350371406

ISBN: (纸本)9798350371413

A critical component of network security is intrusion detection, which involves locating and stopping illegal access. Machine learning has shown potential as a technique for intrusion detection. Using the algorithms efficiently and overcoming the current drawbacks is the key to develop next generation models for intrusion detection system. In this paper, a comparison of various evaluation metrics from different papers is provided. The number of data used for training and name of dataset is listed as well. Analysis is done to provide researchers about the recently implemented algorithms and give a scenario of current challenges that are faced in IDS. In the end, this paper will be a guide for future directions in which researchers can work.

关键词： Deep learning Training Measurement Machine learning algorithms Intrusion detection Information processing Network security

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Breast Cancer Prediction Using Machine Learning

Breast Cancer Prediction Using Machine Learning

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Emerging systems and Intelligent Computing (ESIC), International conference on

作者： Shiv Ashish Dhondiyal Pooja Luthra Bhupender Sharma Rajni Rajni Ishita Uniyal Dept. of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Department of Computer Science & Engineering Chandigarh University Mohali Punjab Department of Computer Science and Application Coer University Roorkee Uttarakhand Department Of Computer Science & Engineering Guru Jambheshwar University of Science and Technology Hisar Haryana India Department of Electrical Engineering Graphic Era Hill University

ISBN: (数字)9798331522100

ISBN: (纸本)9798331522117

The number of newly diagnosed cases of breast cancer exceeds 2.3 million annually worldwide. There is a severe lack of valid prognostic and predictive indicators for the clinical treatment of breast cancer patients at the present time. In order to decrease the mortality rate and increase the survival time of breast cancer patients, early detection of this deadly disease is crucial. Improving breast cancer prognosis relies on the analysis and processing of mammography data, the gold standard for screening and early detection. In order to detect breast cancer in mammograms, the Fuzzy C-means algorithm is employed for image segmentation. Next, we train it to identify the characteristics of the segmented regions, and then we use an effective classifier to categorise the trained pictures into their respective mammography classes. This research used the Breast Cancer Wisconsin Diagnostic dataset and two ML algorithms: K-Nearest Neighbours (KNN) and Random Forest. After getting the results, we compared and evaluated the performance of these two classifiers. Potentially useful tools for both early diagnosis and treatment planning include breast cancer prediction models that account for a wide range of risk factors. Effective disease management involves the collection, storage, and administration of many data sets, as well as intelligent systems based on several aspects for the prediction of breast cancer.

关键词： image segmentation Accuracy Machine learning algorithms Sensitivity Databases Nearest neighbor methods Prediction algorithms Breast cancer Classification algorithms Random forests

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A Machine Learning Based Framework for Pre-processing and Classification of Medical images 1

A Machine Learning Based Framework for Pre-processing and Cl...

引用

1st International conference on Technologies for Smart Green Connected Society 2021, ICTSGS 2021

作者： Beram, Shehab Mohamed Pallathadka, Harikumar Patra, Indrajit Prabhu, P. Department of Computing and Information Systems Sunway University Malaysia Manipur International University Manipur Imphal India NIT West Bengal Durgapur India Department of IT Directorate of Distance Education Alagappa University Karaikudi India

ISBN: (纸本)9781607685395

Disease diagnosis and therapy rely heavily on medical imaging. For example, image processing has been used to depict different cell types using textural aspects in the medical and biological fields, as well as to distinguish between living and dead cells using pictures. There are major difficulties in reproducibility and predictability since traditional techniques of microscopic image analysis rely on a labor-intensive approach with a restricted number of bone samples. To construct a novel technique, image processing in a digital system was used to include several steps, including pre-processing, object representation, feature extraction, classifying, and picture interpretation. It was found that the Wavelet denoising tool was a potent picture enhancing technique. Second-stage preprocessing of pictures is validated by the wavelet, which divides the image into subbands like near, horizontal and vertical estimations. To cure at the beginning stage, any bone fracture or abnormality (cancer) must be anticipated. Human osteosarcoma, a malignant primitive bone tumor, is the most common form of bone cancer in the human body. Metastasis is a term for the spread of stage condition to other sections of the body or to organs. Our proposed work was associated with preprocessing to eliminate noise and get smooth images. This procedure would aid in improving image quality and removing false segments. KNN, CNN, and ANN classification algorithms are used to classify image datasets. © The Electrochemical Society

关键词： Classification Deep Learning K-Means Machine Learning Segmentation Wavelet Denoising

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Learning systems in Forgery image Detection 1st

Learning Systems in Forgery Image Detection

引用

1st International conference on Intelligent systems in Computing and Communications, ISCComm 2023

作者： Shetty, Sudheer Ganesh, Devadiga Likhit kumar Poojary, Rahul R. Raviraj Tejas, R. Hegde, Nagaratna P. Vikkurty, Sireesha Department of ISE Sahyadri College of Engineering & Management Mangaluru Affiliated to Visvesvaraya Technological University Karnataka Belagavi India Department of ISE Alvas Institute of Engineering and Technology Moodbidri Affiliated to Visvesvaraya Technological University Karnataka Belagavi India Department of CSE Vasavi College of Engineering Telangana Hyderabad India

ISBN: (纸本)9783031756078

In an era dominated by the ubiquitous sharing of visual content on social media, the proliferation of fake photographs and digital fakes has emerged as a critical issue. This paper explores how fake images are detected with fusion of by various techniques of image processing, CNN, and machine learning, presenting an innovative approach to address the intricate challenge of identifying manipulated images. This paper gives insight on various approaches of image processing techniques and state-of-the-art machine learning algorithms used in a robust forgery image detection system. A comprehensive framework is proposed on various image forensic methods, ensuring the precise detection of fake images across diverse contexts. The paper also emphasizes the impact of countering the dissemination of fake photographs to preserve the credibility of digital media. This combination of image processing, CNN and machine learning propels the ongoing quest to uncover the truth behind the surge of digital fakes in today's digital landscape. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Phytosense: Cultivating Trust Origins with AI

Phytosense: Cultivating Trust Origins with AI

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Advances in Data Engineering and Intelligent Computing systems (ADICS), International conference on

作者： Narendran. M Sathya. A Ajay Prasad S Ashwin Karthikeyan Harish B Department of IT KCG College of Technology chennai Tamilnadu

ISBN: (数字)9798350364828

ISBN: (纸本)9798350364835

The authentication of medicinal plants is crucial for ensuring the integrity of the pharmaceutical supply chain. This paper presents a novel approach utilizing machine learning for the identification of medicinal plants to enhance authenticity and supply chain integrity. Our proposed software leverages advanced image processing techniques to analyze plant images. Identify key features indicative of specific species. Through a comprehensive literature. Review, we explore the existing methodologies and limitations in this domain. We then introduce our system architecture, detailing the frontend and backend components, application logic, and storage mechanisms. The core of our approach lies in the development of an efficient algorithm, which we discuss along with its advantages, disadvantages, and potential alternatives. Additionally, we outline the various modules comprising our software and their functionalities. Finally, we conclude by discussing the implications of our work and suggesting avenues for future research and development.

关键词： Reviews image processing Supply chains Software algorithms systems architecture Machine learning Data engineering

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Faster image Deblurring for Unmanned Aerial Vehicles

Faster Image Deblurring for Unmanned Aerial Vehicles

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International conference on Unmanned Vehicle systems-Oman (UVS)

作者： Viktor Sineglazov Kyrylo Lesohorskyi Olena Chumachenko dept of Aeronavigation Electronics and Telecommunication National Aviation University Kyiv Ukraine dept of Artificial Intelligence IASA National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute” Kyiv Ukraine

This work is devoted to the development of a novel deep learning encoder-decoder algorithm for real-time noise and blur elimination in video frames, received from UAV. This work improves on existing algorithms by providing a more flexible blind deblurring solution than existing kernel-based methods. The proposed method can be applied to both improve the drone operator's capabilities and to improve the performance of autonomous image processing tasks, such as object identification and visual navigation systems. Different types of blur as well as possible types of noise are presented. A brief overview of existing methods is provided. The problem of frame alignment due to the object's movement and associated noise is considered. Existing deblurring and image restoration methods are reviewed, including state-of-the-art. Their limitations are highlighted. To solve the limitations a method based on a fully convolutional encoder-decoder network with residual connections is presented. Dataset generation and training procedures are discussed. The approach is then compared to existing state-of-the-art deep learning methods. The proposed method enables up to 9 times faster blind image restoration with comparable quality in comparison to existing state-of-the-art image restoration methods.

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