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检索条件"任意字段=Proceedings of the 3rd International Conference on Video and Image Processing"

共 12962 条记录，以下是31-40 订阅

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Accuracy of image Registration During Similarity Transformation 3rd

Accuracy of Image Registration During Similarity Transformat...

引用

3rd international conference on Machine Vision and Augmented Intelligence, MAI 2023

作者： Babayan, Pavel Kozhina, Ekaterina Gagarin Str. Ryazan State Radio Engineering University Named After V.F. Utkin 59/1 Ryazan390005 Russia

ISBN: (纸本)9789819743582

This paper considers the problem of assessing the accuracy of image registration using a multi-reference algorithm. This algorithm is based on a random selection of reference areas in the image, with the help of which geometric transformations are estimated. The similarity image transformation model is used in the work. The novelty of this article lies in the fact that, unlike other articles, methods for assessing the accuracy of image registration are proposed to improve the quality of image analysis. The correlation matrix of errors in the estimation of the parameters of geometric transformations and the correlation matrix of registration errors for the entire image have been determined. The nature of the dependence of the registration error variance on the zone of choice of reference areas is shown. Experimental studies were carried out on images obtained by semi-natural modeling based on video captured by a vehicle traffic control system. The research results can be applied when developing image processing algorithms, for example, image stabilization, registration, and moving object detection. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： image correlation

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A Vision-based System for Real-Time Monitoring of Water Quality in Natural and Artificial Reservoirs 3

A Vision-based System for Real-Time Monitoring of Water Qual...

引用

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

作者： Bhatlawande, Shripad Mohole, Shrihari Pahade, Yash Shilaskar, Swati Munjale, Aditya Vishwakarma Institute of technology Pune India

ISBN: (纸本)9798331527549

Manual sampling and laboratory testing are the mainstays of traditional water quality monitoring methods, which causes delays in data processing and decision-making. This study proposes an enhanced monitoring of water quality system that uses machine learning algorithms to classify and segment water samples in real-time, thereby overcoming these restrictions. Water samples are classified as "Clean" or "Polluted" having an accuracy of 83.99% using a CNN model. The CNN model, trained over 20 epochs, effectively learns patterns from the dataset to deliver precise results. Additionally, a U-Net model is implemented for image segmentation, generating masks that accurately highlight areas of interest within the water samples, with a segmentation accuracy of 68%. Streamlit's intuitive interface makes it simple for users to upload images and get immediate feedback on water quality. This system helps to improve the health of the environment monitoring with automating the method of water quality evaluation. It does this by providing a simpler, quicker, and scalable solution that enables prompt, data-driven actions. © 2025 IEEE.

关键词： image segmentation

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Three quantum neural network architectures for multi-class image classification 3

Three quantum neural network architectures for multi-class i...

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3rd international conference on Algorithms, Network, and Communication Technology, ICANCT 2024

作者： Qu, Guangkai Yu, Xianglong Wang, Zhimin Gu, Yongjian Faculty of Information Science and Engineering Ocean University of China Qingdao266100 China

ISBN: (数字)9781510688902

ISBN: (纸本)9781510688896

Quantum neural networks (QNNs) integrate the advantages of quantum information with the formidable capabilities of classical neural networks, exhibiting notable potential in image classification tasks. However, the performance of current QNNs in processing multi-class image classification tasks still leaves room for improvement. In this paper, we propose three QNN architectures to enhance the accuracy of multi-class image classification by utilizing techniques of overparameterization, dense pooling, and local feature extraction. These networks are capable of mapping conventional data to the amplitudes of quantum states via amplitude encoding, eschewing the need for traditional dimensionality reduction techniques. Following processing with parameterized quantum circuits (PQCs), we conduct quantum measurements to extract feature information. We validate the proposed architectures using the MNIST dataset for eight-class image classification problems. The experimental results unequivocally demonstrate that the three techniques we employed significantly enhance the accuracy of QNNs in multi-class image classification tasks. © 2025 SPIE.

关键词： Quantum electronics

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Design and Implementation of Overseas Communication Path for Chinese Corporate image Based on Multimodal Discourse Analysis with Machine Learning Algorithm 24

Design and Implementation of Overseas Communication Path for...

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3rd international conference on Artificial Intelligence and Intelligent Information processing, AIIIP 2024

作者： Lin, Jiling Lei, Fei Liu, Weiliang School of Foreign languages Guangdong University of Science & Technology Guangdong Dongguan China School of Finance and Economics Guangdong University of Science & Technology Guangdong Dongguan China Huizhou Modern Vocational Technology School Guangdong Huizhou China

ISBN: (纸本)9798400707308

In an increasingly globalized economy, the communication of corporate image plays a pivotal role in shaping perceptions and fostering relationships with international stakeholders. This study explores the design and implementation of effective overseas communication strategies for Chinese corporations, leveraging multimodal discourse analysis and machine learning algorithms. By synthesizing quantitative and qualitative data, we analyze various communication modalities—including visual, textual, and auditory elements—utilized in corporate branding across different cultural contexts. Employing machine learning techniques, we identify patterns and insights from vast datasets, enabling a nuanced understanding of how multimodal elements influence audience reception and engagement. Our findings reveal key factors that contribute to successful corporate image dissemination and highlight the importance of cultural sensitivity in communication strategies. The research provides a framework for Chinese corporations to enhance their global presence by integrating data-driven insights into their communication practices, ultimately fostering a more favorable corporate image and stronger international relationships. This study not only contributes to the field of corporate communication but also offers practical implications for practitioners seeking to navigate the complexities of multicultural environments in an era of rapid technological advancement. © 2024 Copyright held by the owner/author(s).

关键词： Machine Learning Algorithms

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3rd international conference on Machine Vision and Augmented Intelligence, MAI 2023

3rd International Conference on Machine Vision and Augmented...

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3rd international conference on Machine Vision and Augmented Intelligence, MAI 2023

ISBN: (纸本)9789819743582

The proceedings contain 76 papers. The special focus in this conference is on Machine Vision and Augmented Intelligence. The topics include: Survey on Robustness of Deep Learning Techniques on Adversarial Attacks in WBAN;synergizing Collaborative and Content-Based Filtering for Enhanced Movie Recommendations;exploring Transformer-Based Approaches for Hyperspectral image Classification: A Comparative Analysis;deep Learning for Cognitive Task and Seizure Classification with Hilbert–Huang Transform and Variational Mode Decomposition;tracking of Ship and Plane in Satellite videos Using a Convolutional Regression Network with Deep Features;Tumor Detection and Analysis from Brain MRI images Using Deep Learning;software Maintenance Prediction Using Stack Ensemble Deep Learning Algorithms;resource Allocation in 6G Network for High-Speed Train Using D2D Outband Communication;controlling the Band-to-Band Tunneling Effect in Charge Plasma Based Dopingless Transistor;Comparison of Different CIC Filter Architectures on the Basis of a Novel Parameter Called Noise Factor for Sigma-Delta Based ADCs;the Scientific Analysis on Effective Yoga Posture Recognition Techniques;impact of Gamma Rays on Emerging Devices for Photonic Applications;shaft Rotation Monitoring Using Radar Signal processing and Wavelet Transform;gysel Power Divider Miniaturization Using an Inter-Digital Capacitor-Based Slow-Wave Structure;noise Estimation and Removal in Fundus images Using Pyramid Real image Denoising Network;evaluation of Hybrid Encryption Method to Secure Healthcare Data;multimodal Face Recognition System Using Hybrid Deep Learning Feature;Classification of Copy and Move image by Using HELM-FSK Method: An Efficient Approach;analysis of Energy Efficient Smart Home Based on IoT System;role of Explainable Artificial Intelligence Approaches in Cybersecurity.

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Deep Learning for Automated image Captioning: A CNN and Transformer Model Analysis 3

Deep Learning for Automated Image Captioning: A CNN and Tran...

引用

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

作者： Saarika, Kathula Varsha, Vasaraju Sri Harsha, P. Sheikh, Anjum Nabi Dept. Artificial Intelligence and Data Science Chaitanya Bharathi Institute of Technology India

ISBN: (纸本)9798331527549

Automatic image captioning is a multidisciplinary effort that combines machine learning, natural language processing, and computer vision in creating meaningful and context-rich captions for images. Within a short time, this research problem has garnered a lot of attention and new techniques are continuously being proposed. However, there are many dismaying factors as well. For instance, the current models still have a long way to go in attaining human accuracy. This paper seeks to conduct a systematic review of the recent trends and developments in the field of image captioning techniques and models with a focus on 2023 and 2024 by systematically analyzing the 13 publications located in Springer and IEEEXplore libraries. According to our research, Convolutional Neural Networks allow the identification of image content at an early stage, and Recurrent Neural Networks or Long Short-Term Memory networks usually appear at the text generation stage. The most common image datasets are MS COCO and Flickr8k. Almost all studies utilized the BLEU scores (1, 2, 3, and 4) mechanism. It is alleviated that CNN with LSTM models tends to perform better when compared to RNN-modeled networks. Attention mechanisms and Encoder-Decoder architecture are aggressively becoming the two major solutions to strengthening image captioning. This review will help and provide direction to those who are interested in practicing research that aims to promote the techniques and processes of automatic image captioning. © 2025 IEEE.

关键词： Convolutional neural networks

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Spread-Spectrum Based a New Secured Robust imageWatermarking Scheme 3rd

Spread-Spectrum Based a New Secured Robust ImageWatermarking...

引用

3rd international conference on Network Security and Blockchain Technology

作者： De, Supriyo Maity, Soumalya Bhaumik, Jaydeb Pal, Pabitra Giri, Debasis Maulana Abul Kalam Azad Univ Technol Dept Comp Applicat Nadia 741249 India Maulana Abul Kalam Azad Univ Technol Dept Informat Sci PG Nadia 741249 India Jadavpur Univ Dept Elect & Telecommun Engn Kolkata 700032 India Maulana Abul Kalam Azad Univ Technol Dept Informat Technol Nadia 741249 India

ISBN: (纸本)9789819780501;9789819780518

Nowadays, the advancement of modern communication systems, along with Internet usability, enhances the application of multimedia data in various fields. In this context, security in multimedia data is highly demanding to prevent unauthorized access, as well as to ensure confidentiality. This paper introduces a new reversible blind imagewatermarking scheme to validate the image authentication and confidentiality for secret information. To address this aim, a spread-spectrum-based message hiding technique is developed. The reduced Lorenz map combined with the sine-cos map is employed here to produce a key-dependent pseudo-random number (PRN), which is directly mapped into the cover image according to the bit pattern of the secret message. On the other hand, the message hiding has been performed in the cover image by decomposing it using a two-level integer wavelet transform (IWT). The robustness of the scheme is further improved by employing Hamming (7, 4) code and a geometric distortion correction module. Several cover images are used to examine the effectiveness of the proposed scheme. The experimental results present that this approach has better or equivalent robustness against common sig-D nal processing distortions and geometric distortions compared to relevant existing schemes.

关键词： Blind image watermarking Reversible IWT Chaos-based PRN Spread-spectrum Robustness Imperceptibility

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Artificial Intelligence (AI) Assisted Literature Review 3

Artificial Intelligence (AI) Assisted Literature Review

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

作者： Hilda, Jabanjalin Jeevaa, S.V. Bhadhrinath, U. Solanki, Darshini Sr. School of Computer Science Engineering Vellore Institute of Technology Vellore India Vellore Institue of Tehnology Vellore Vellore India

ISBN: (纸本)9798331527549

This research aims to address the inefficiencies and inaccuracies in traditional literature review methodologies by integrating advanced Artificial Intelligence (AI) technologies, specifically transformer-based models. The study focuses on optimizing PDF-to-text conversion through image and character recognition, thereby minimizing resource wastage and errors associated with manual transcription. Additionally, the research employs advanced Natural Language processing (NLP) techniques to automate the identification of critical phrases such as keywords and organizational entities, significantly reducing manual errors and improving accuracy. The methodology also includes the use of sophisticated summarization algorithms and sentiment analysis, streamlining the process of summarizing extensive literature and identifying study limitations. By leveraging state-of-the-art transformer models and industry-standard libraries, this research introduces a novel, efficient framework for literature analysis. The outcome is a comprehensive, AI-powered toolkit that enhances the speed, accuracy, and precision of literature reviews, ultimately providing researchers with a more reliable and resource-efficient approach to navigating scholarly content. © 2025 IEEE.

关键词： Natural language processing systems

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Experimental Study on Infrared Detection and De-Icing of Icing Cable 3

Experimental Study on Infrared Detection and De-Icing of Ici...

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3rd international conference on Electrical, Electronics, and Information Engineering, EEIE 2024

作者： Shen, Jianhao Li, Jingyu Hu, TianKai Li, Zhenmin Li, Qingying School of Air Transportation Shanghai University of Engineering Science Shanghai201620 China

ISBN: (纸本)9781510688179

Ice accumulation on cables can severely compromise the stability and safety of power transmission, making efficient detection and de-icing of icing cable critically important. This study proposes a method for cable icing detection and deicing based on infrared technology. By integrating infrared detection and heating technologies, real-time icing detection and rapid de-icing are achieved. The icing state on the cable surface is analyzed using infrared imaging and image processing techniques. The accuracy of icing detection and the effectiveness of de-icing are validated by comparing infrared images and data distributions before and after de-icing. Experimental results demonstrate that the integrated infrared detection and de-icing system exhibits reliable performance in cable de-icing, providing a novel solution for cable maintenance in low-temperature environments. © The Authors.

关键词： de-icing icing cable image process infrared detection

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3rd international conference on Computing and Communication Networks, ICCCN 2023

3rd International Conference on Computing and Communication ...

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3rd international conference on Computing and Communication Networks, ICCCN 2023

ISBN: (纸本)9789819726707

The proceedings contain 60 papers. The special focus in this conference is on Computing and Communication Networks. The topics include: Enhancing Security in Wireless Sensor Networks: A Broadcast/Multicast Authentication Framework with Identity-Based Signature Schemes;TSFTO: A Two-Stage Fuzzy-Based Tasks Orchestration Algorithm for Edge and Fog Computing Environments;tracking Climatic Variations Through Smart IoT-Driven Approach: An Exploratory Analysis;Design and Manufacturing of an Efficient Low-Cost Fire Surveillance System Based on GSM Communications and IoT Technology;a Comparative Analysis of Optimized Routing Protocols for High-Performance Mobile Ad Hoc Networks;integrating Sandpiper Optimization Algorithm and Secure Aware Routing Protocol for Efficient Cluster Head Selection in Wireless Sensor Networks;the Effects of Weather Conditions on Data Transmission in Free Space for Wireless Sensor Networks;Using Wireless Sensor Network Performance and Optimizing in Underground Mines with Virtual MIMO Antenna;a Novel Logistic Regression-Based Fire Detection Model Using IoT in Underground Coal Mines;An MQTT IoT Intrusion Detection System Using Deep-Learning;multi-objective Optimal Feature Selection for Cyber Security Integrated with Deep Learning;application of Gradient Boosting Classifier-Based Computational Intelligence to Detect Drug Addiction Threat in Society;insurance 4.0: Smart Insurance Technologies and Challenges Ahead;review on Vision Transformer for Satellite image Classification;primitive Roots and Euler Totient Function-Based Progressive Visual Cryptography;approach for Fire Detection Using image processing;review of Various Approaches for Authorship Identification in Digital Forensics;machine Learning Model for Traffic Prediction and Pattern Extraction in High-Speed Optical Networks;improved Markov Decision Process in Wireless Sensor Network for Optimal Energy Consumption.

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