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检索条件"任意字段=3rd International Conference on Computer Vision Theory and Applications"

共 3942 条记录，以下是11-20 订阅

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Exploring the Potential of MANETs: A Study on Raspberry Pi and UAV Integration 3

Exploring the Potential of MANETs: A Study on Raspberry Pi a...

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3rd international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Archana, B. Mahalakshmi, A. Revathi, K. Ashok Kumar, K. Hema, A.B. Dept. of Computer Science and Engineering Sri Ramakrishna Engineering College Coimbatore India Dept. of Information Technology SNS College of Engineering Coimbatore India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527549

This research investigates the feasibility of utilizing Mobile Ad Hoc Networks (MANETs) in conjunction with Raspberry Pi-equipped Unmanned Aerial Vehicles (UAV) swarms. The primary objective is to overcome the limitations of traditional communication infrastructures in dynamic and decentralized environments. Specifically, we address the challenge of establishing reliable and efficient communication links within a swarm of UAVs engaged in various applications, including disaster response, military reconnaissance, and environmental monitoring. By integrating Raspberry Pi devices with UAVs, we aim to create a self-organizing communication network that enables seamless data transmission, coordinated swarm operations, and enhanced mission effectiveness. The research explores different synchronization strategies, including master-slave and decentralized approaches, to optimize swarm coordination. Furthermore, we investigate the potential of incorporating advanced technologies such as machine learning, computer vision, and swarm intelligence to further enhance the autonomy, adaptability, and overall performance of the UAV swarm. © 2025 IEEE.

关键词： Mobile ad hoc networks

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YOLOv8: A Novel Vehicle Mirror Detection Algorithm with Enhanced Performance using Deep Learning 3

YOLOv8: A Novel Vehicle Mirror Detection Algorithm with Enha...

引用

3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Narayanan, Sundarakrishnan Donthi, Yashas Mohana Ramakanth Kumar, P. Sreelakshmi, K. Rv College of Engineering® Center for Computer Vision Research Bengaluru India Rv College of Engineering® Center for Computer Vision Research Bengaluru India

ISBN: (纸本)9798331509675

In recent times, Artificial intelligence (AI) and machine learning (ML) technologies have demonstrated remarkable capabilities in addressing real time needs and challenges across multiple domains and fields. In the world of deep learning, various Neural Network architectures like DNN, CNN, and RNN have been instrumental in advancing solutions to complex problems spanning multiple industries, business environments and law enforcement use cases. This paper implemented a special type of CNN, YOLOv8(You Only Look Once), a SOTA architecture to detect the presence of rear-view mirrors in two-wheelers for traffic surveillance and law enforcement applications. The proposed research introduces a novel idea in the realm of road safety by presenting the first ever paper on vehicle mirror detection. This approach sheds light on an often-overlooked aspect, contributing significantly to the enhancement of road safety measures. A custom dataset of 1246 training images, 155 test images and 157 validation images has been considered. This dataset has been used to train and test a binary classification model with the label "mirror". Images with the label "mirror"indicate the presence of one or both rear-view mirrors on two-wheelers. On the other hand, images with no mirrors are treated as "background"images. The model achieved a maximum precision of 93.8%, recall of 87.9%, mAP50 of 94.1% and F1-score of 90.9%. These results stand as evidence of the robustness and proficiency of the model, ensuring that it can be potentially used in real world use cases, specifically for law enforcement applications. © 2025 IEEE.

关键词： Convolutional neural networks

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Face Recognition using MTCNN, Inception - Resnet with Ensemble Approach 3

Face Recognition using MTCNN, Inception - Resnet with Ensemb...

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3rd international conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Mahanthesha, U. Tejaswini, R.M. Nagarathna, C.R. Ravikumar, Y. Tejal Bnm Institute of Technology Dept.of Ai and Ml Bangalore India University of Manchester Dept of Advanced Computer Science United Kingdom

ISBN: (纸本)9798331515911

One of the critical task included in the computer vision in face recognition. Security system, surveillance system, computer-human interactions are some of the applications where face recognition has been widely used. This paper proposes an novel face recognition model that inculcates deep learning and feature extraction techniques. Our model, named "Inception-Resnet"is built to efficiently and accurately recognize faces from images. Inception-Resnet employs a deep convolutional neural network and it also employs a deep CNN architecture that includes multiple layers of convolutional and pooling operations for robust feature extraction. The network is trained using a large-scale dataset consisting of labelled face images, that allows it to learn differential facial features. Additionally, Inception-resnet utilizes a loss function during training phase to encourage the network to learn compact and separable face embeddings in the feature space. Face verification is conducted using an ensemble approach which combines multiple classifiers, including Support Vector Machine (SVM), K-Nearest Neighbours (KNN), and logistic regression, for the classification of face embeddings. © 2025 IEEE.

关键词： Convolutional neural networks

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A Review on Meta-Learning: How Artificial Intelligence and Machine Learning Can Learn to Adapt Quickly 3

A Review on Meta-Learning: How Artificial Intelligence and M...

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3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Khadse, Shrikant Gourshettiwar, Palash Pawar, Adesh Faculty of Engineering and Technology Wardha442001 India Faculty of Engineering and Technology Department of Computer Science and Medical Engineering Wardha442001 India Department of Computer Science and Medical Engineering Maharashtra Wardha442001 India

ISBN: (纸本)9798331509675

Meta-learning aims to create Artificial Intelligence (AI) systems that can adapt to new tasks and improve their performance over time without extensive retraining. The advent of meta-learning paradigms has fundamentally changed how Artificial Intelligence (AI) and Machine Learning (ML) are handled, constantly reshaping these fields. Research study reviews the broad field of meta-learning, highlighting its critical function in bridging the AI-ML divide to improve adaptability. The study starts with a review of past developments, following the development of meta-learning algorithms and their underpinning theories. The historical view lays the groundwork for understanding the course of this dynamic regulation, spanning from early efforts in probabilistic program induction to modern discoveries in model-agnostic meta-learning. The study explores multiple meta-learning applications in AI and ML, revealing how they affect financial forecasting, computer vision, natural language processing, autonomous cars, health informatics, robotics, and personalized recommendation systems. When seen from the perspective of adaptive intelligence, meta-learning may be effectively applied to conditions with limited data, hyperparameter optimization, few-shot learning, and quick adaption in reinforcement learning settings. © 2025 IEEE.

关键词： Contrastive Learning

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Smart ANPR: A Checkpoint-Based Toll and Parking Management System Using Hybrid OCR and Blockchain 3

Smart ANPR: A Checkpoint-Based Toll and Parking Management S...

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3rd international conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Samarth, M.N. Gopisetti, Sampriti Chaitra, M. Bnm Institute of Technology Affiliated to Vtu Dept. of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798331515911

Automated Number Plate Recognition is one of the most important applications of computer vision, employed in the enforcement of traffic law, vehicular management, security, and even at toll collection. This paper proposed an advanced system for ANPR that can move away from the traditional booth and establish a checkpoint. The strategically located cameras with checkpointing will automatically capture the plates of passing vehicles, permitting road tax and parking fees to be deducted without making contact. A hybrid approach that combines the capabilities of OpenCV, Tesseract OCR, and deep-learningbased OCR models helps tackle such challenges as stable images from live video feeds and highly accurate text recognition. To this end, since the system integrates high-resolution frame stabilization using OpenCV to ensure crystal clear captures, it further employs the use of YOLOv8 to boost the precision of real-time detection. The blockchain technology is further implemented to ensure that transactions are tamper proof, therefore ensuring secure and transparent billing systems. This makes the ANPR solution deployable on edge devices, which opens real-time processing capabilities for high-speed and high-volume environments, thereby offering a scalable and efficient approach for intelligent transportation and smart city applications. © 2025 IEEE.

关键词： Blockchain

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Smart Attendance By Using Face Recognition 3

Smart Attendance By Using Face Recognition

引用

3rd international conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Kumar, Naveen Vasantha Kumari, N. Sheetal Alli Presidency College Department of Computer Applications Bangalore India

ISBN: (纸本)9798331515911

Maintaining attendance by hand in contemporary classrooms is a laborious activity that is prone to errors and mistakes. To make this process easier to comply with, we suggest a Face Recognition Based Attendance Management System (FRAMS) which makes use of modern facial recognition technology to eliminate the need for manual attendance. This system utilizes computer vision technology where face detection and recognition take place using real time python open cv software while mingling through a comfortable user interface designed by Tkinter. Haar Cascade classifiers are incorporated for face detection while the facial recognition aspect is conducted through an efficient algorithm referred to as Local Binary Patterns Histograms (LBPH) that is resistant to changes in lighting, facial emotions and facial *** works by taking students pictures using a webcam and a dataset is generated from these pictures. This dataset is then used to prepare the face recognition model. During attendance sessions, live faces are compared against the stored database within the system to recognize and verify users. When the system is capable to identify a person, it automatically registers the person's state using the online spreadsheet which further provides attendance records in a precise manner. The aided system is efficient as it removes the need for attendants roll call, thereby minimizing errors and curtailing proxy *** so, the system was subjected to extensive testing when in use in various situations and its recognition accuracy rate was high in controlled conditions. There were, however, some problems like the differences in lighting and the chances of wrongful identification in the middle of busy pictures. Even so, in light of these challenges, FRAMS is an improvement over the attendee sheets that yesteryear utilized because it's faster and more dependable. For works in progression, it may be possible to enhance the recognition algorithms to cope with increasing

关键词： User interfaces

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A Game theory-based Reverse Vickrey Auction for Dynamic Pricing in Edge Computing 3

A Game Theory-based Reverse Vickrey Auction for Dynamic Pric...

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3rd international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Rasane, Atharva Tapale, Manisha Dept. Computer Science and Engineering KLE Technological University’s Dr. M. S. Sheshgiri College Belagavi India

ISBN: (纸本)9798331527549

Low-latency communication is critical for effectively functioning Internet of Things (IoT) systems, including applications in healthcare monitoring, industrial automation, and smart cities. However, the vast number of IoT Devices often overwhelms cloud providers, necessitating the adoption of edge computing to reduce latency and distribute computational load. This paper introduces a novel dynamic pricing mechanism for edge computing based on a reverse Vickrey auction, leveraging game theory to balance competitive pricing with profitability for Edge Servers. We develop utility functions for both IoT Devices and Edge Servers, optimizing the markup to maximize edge server utility while adhering to the budget constraints of IoT Devices. Simulations were conducted with 10 Edge Servers and 50 IoT Devices over 2,000 time units. Results show that the proposed mechanism significantly reduces wait times, with most tasks completed within 10-time units. Moreover, dynamic pricing increases Edge Server profits by up to 10 times in some instances compared to fixed markup approaches. Notably, one server’s total profit rose from 7,702 to 76,130 and another from 57,965 to 94,298, underscoring the enhanced profitability and utilization. These findings confirm that our reverse Vickrey auction strategy effectively balances low-latency demand with sustainable operational revenue for Edge Servers, offering a robust and scalable solution for edge-centric IoT environments. © 2025 IEEE.

关键词： Budget control

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From vision to Well-Being: A Functional Classification of Leadership Studies in Healthcare 3

From Vision to Well-Being: A Functional Classification of Le...

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3rd international conference on Digitalization and Management Innovation, DMI 2024

作者： Efremov, Ljupcho Dimitrievska, Vera Liberal Arts Department American University of the Middle East Kuwait University of American College Skopje Macedonia

ISBN: (纸本)9781643685823

Leaders provide a clear vision of what they want to achieve and set the overall direction for a company or team in order to be successful. More specifically leadership in healthcare is crucial for ensuring high-quality patient care, staff well-being, and the overall success of healthcare organization. This is especially challenging in healthcare setting where patients are considered as unique category of customers and sometimes physicians have dual roles as doctors and leaders. The aim of this research is to identify type of leadership studies conducted in healthcare setting. The methodology used was rapid review. The results show that there are three types of studies: studies with focus on transformational and transactional leadership theory in the healthcare industry, studies with focus on effectiveness of leadership in healthcare and studies related to contemporary leadership for change in healthcare. The abovementioned classification about leadership in the healthcare setting provide solid basis for recommendations about better informed choices and better coordination, streamlined processes, and ultimately, improved patient outcomes in the healthcare industry. © 2025 The Authors.

关键词： Functional Classification Healthcare Leadership Rapid Review

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Deep Learning Technique for computer vision-Based Pose Estimation for Augmented Reality 3rd

Deep Learning Technique for Computer Vision-Based Pose Estim...

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3rd international conference on Intelligent Computing Techniques for Smart Energy Systems, ICTSES 2023

作者： Palanimeera, J. Ponmozhi, K. Jyothi, Kanagaraj Dargar, Shashi Kant Birla, Shilpi Department of Computer Applications Sathyabama Institute of Science and Technology Chennai India Department of Computer Applications Kalasalingam Academy of Research and Education Krishnan Kovil Tamil Nadu Virudhunagar India Department of Biotechnology Kalasalingam Academy of Research and Education Krishnan Kovil Tamil Nadu Virudhunagar India Department of Electronics and Communication Engineering Kalasalingam Academy of Research and Education Krishnan Kovil Tamil Nadu Virudhunagar India Department of Electronics and Communication Engineering Manipal University Jaipur Rajasthan Jaipur India

ISBN: (纸本)9789819784288

Pose estimation approaches of open pose were suggested. Pose estimation in real-time is a difficult research subject with many computer vision applications. Pose estimation applications are Human Activity Estimation, Motion Transfer and Augmented Reality, Training Robots, Motion Tracking for Consoles, and Human Fall Detection. This paper describes the Augmented Reality (AR) of pose estimation. AR is an enhanced version of the real physical world that is achieved through the use of digital visual elements, sound, or other sensory stimuli delivered via technology. One interesting use for obtaining the Open-Pose discovered important locations in each pose is in the field of augmented reality. A technique for 3D model overlays on an image using pose key point information that is presented in this study. To extract crucial information from the data, the fully convolutional networks deep learning technique is used. The technique used in this work comprises the real-time intersection of the picture plane with the intended 3D model and accurate model orientation based on the key point data. The performance will greatly depend on the Graphics Processing Unit specifications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep learning

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Faster-RCNN in Human Detecting on Thermal Images 3rd

Faster-RCNN in Human Detecting on Thermal Images

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3rd international conference on Advances in Information and Communication Technology, ICTA 2024

作者： Anh, Le Tuan Ngan, Tran Thi Dung, Vu Viet Luc, Do Dinh Nguyen, To Huu Thai Nguyen University of Information and Communication Technology Thai Nguyen Viet Nam International School Vietnam National University Hanoi Viet Nam

ISBN: (纸本)9783031809422

By the development of Artificial Intelligence, many applications of deep learning and computer vision algorithms in object detection on color images were introduced. The obtained results get higher accuracy than other classical methods. However, detecting objects on thermal images has not received much research attention. In this paper, we focus on researching and proposing the use of Faster-RCNN deep learning model in order to detect people on thermal images. The proposed model consists of two phases: Data processing and model applying. The experiments on a thermal image dataset, namely as Deep Thermal Outdoor, are performed to evaluate performance of the proposed model and compare it to YOLO and SSD models. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Contrastive Learning

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