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Virtual Reality image Generation and Enhancement based on Real-Time Model Simplification 5th

Virtual Reality Image Generation and Enhancement Based on Re...

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5th international conference on 3D Imaging Technologies-Multidimensional signal Processing and Deep Learning, 3DIT-MSP and DL 2023

作者： Luo, Lilei Shandong Vocational College of Science and Technology Shandong Weifang261053 China

ISBN: (纸本)9789819751839

Virtual reality technology is a new practical technology that comes directly from the application and involves many disciplines. It is a comprehensive and integrated technology that combines advanced computer technology, sensing and measurement technology, simulation technology, and microelectronics technology. The virtual image is a dynamic image expression in the field of visual image, which is digitally generated by a computer and has its own unique image construction method. As the application of virtual reality technology deepens, the complexity and realism of virtual scenes are required more and more, far exceeding the processing capacity of computer graphics hardware. Therefore, there is an urgent need to resolve the contradiction between the complexity of the scene and the real-time nature of the interaction. In this paper, based on the review of related literature, virtual reality image generation methods and augmentation technology methods are reviewed. Several problems of simplification of multi-resolution models based on computer technology and the classification of algorithms are constructed, and the practical application of multi-resolution models in solving the problems of complexity and real-time scenes is discussed. It is demonstrated that the realism and practicality of the images are achieved by constructing smooth curve models and reducing the coupling of image errors so that the virtual reality images are generated with improved accuracy and clarity for better research use. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Virtual environments

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Evaluation of Art Works based on Expert System and Improved image Algorithm 5th

Evaluation of Art Works Based on Expert System and Improved ...

引用

5th international conference on 3D Imaging Technologies-Multidimensional signal Processing and Deep Learning, 3DIT-MSP and DL 2023

作者： Li, Liangci School of Art & Design Hubei University of Technology Hubei Wuhan430000 China

ISBN: (纸本)9789819751839

In order to improve the quality of digital evaluation of works of art, this paper combines an expert system and an improved image algorithm to build an evaluation model of works of art, realizes rapid analysis of works of art through the intelligent model, puts forward some image features for classification of works of art, and uses multi-core learning to classify images of works of art. The purpose of this paper is to provide some objective criteria for experts by using image classification technology and to assist experts in art works to identify the text information such as the origin, theme, and age of art works more accurately. In order to extract coloring style information of works of art, we measure the number of colors in an image by color saturation and then extract style information by analyzing the color distribution of a colored area. The research results of this paper show that the evaluation system of art works based on expert system and improved image algorithm can effectively improve the digital processing effect of art works and enhance the intelligent evaluation efficiency of art works. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Expert systems

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Multiview heterogeneous graph representation learning with fusion of high-order information and low-order information

Multiview heterogeneous graph representation learning with f...

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2024 international conference on image, signal Processing, and Pattern Recognition, ISPP 2024

作者： Sun, Dengdi Meng, Yundong Luo, Bin Ding, Zhuanlian School of Artificial Intelligence Anhui University Hefei230601 China School of Computer Science and Technology Anhui University Hefei230601 China School of Internet Anhui University Hefei230039 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei230026 China

ISBN: (纸本)9781510680425

Graph neural networks (GNNs) are extensively employed in the analysis of data structured as graphs and excel at learning intricate node relationships. However, traditional GNN methods encounter difficulties when applied to analyzing heterogeneous graphs (HGs) with diverse node types and relationships, due to the complex heterogeneity and semantics of the data. Therefore, we propose Multi-view Heterogeneous Graph Representation Learning with Fusion of High-Order Information and Low-Order Information(MHGRL). MHGRL produces node embeddings by integrating node content transformation, meta-path-based intra- and inter-aggregation, as well as the amalgamation of low- and high-order information from multiple perspectives. Comprehensive experiments on three diverse heterogeneous graph datasets reveal the significant benefits of our MHGRL in node classification endeavors. © 2024 SPIE.

关键词： Contrastive Learning

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Optimization method for 3D visualization of mobile medical images based on user interaction prediction

Optimization method for 3D visualization of mobile medical i...

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2024 international conference on image, signal Processing, and Pattern Recognition, ISPP 2024

作者： Zhai, Tongtong Cui, Yong Lin, Yusong School of Cyber Science and Engineering Zhengzhou University Zhengzhou China School of Computer and Communication Engineering Zhengzhou University of Light Industry Zhengzhou China Collaborative Innovation Center for Internet Healthcare Zhengzhou University Zhengzhou China Hanwei IoT Institute Zhengzhou University Zhengzhou China

ISBN: (纸本)9781510680425

With the advancement in mobile device performance and the growing demand for 3D visualization of medical images, achieving efficient 3D visualization on mobile devices has become paramount. In this study, we dynamically select rendering methods and quality based on different interaction methods and process dynamics during the user exploring volume data, aiming to swiftly address user interaction requests. Additionally, we employ a trajectory prediction model by leverage of LSTM+MDN to discern inherent patterns in virtual camera parameter sequences from users' historical interaction statistics, predicting virtual camera motion trajectories for pre-rendering and further reducing waiting time for remote rendering. Through comprehensive experimental evaluations encompassing various network conditions, medical image data, and devices, we validate the efficacy of the proposed rendering optimization strategy and prediction model. The experimental outcomes demonstrate a 10% reduction in overall network transmission volume while preserving image quality and response time, thus enhancing user satisfaction with high-quality visualization and interactive exploration of mobile medical images. © 2024 SPIE.

关键词： Rendering (computer graphics)

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Performance Evaluation and Proposed Enhancement of latest image Watermarking Schemes against specific forgery attacks 7

Performance Evaluation and Proposed Enhancement of latest Im...

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7th international conference on internet Applications, Protocols, and Services, NETAPPS 2024

作者： AlHassan, Adnan Nadeem Ashraf, Muhammad Mehmood, Amir Almoamari, Hani Alqahtany, Saad Said Sarim, Muhammad Abbasi, Qammer H. Faculty of Computer and Information Systems Islamic University of Madinah Madinah42351 Saudi Arabia Department of Physics Federal Urdu University of Arts Science and Technology Karachi Pakistan Department of Computer Science Al-Kawthar University Karachi Pakistan Department of Computer Science Federal Urdu University of Arts Science and Technology Karachi Pakistan University of Glasgow Glasgow United Kingdom

ISBN: (纸本)9798331519513

Detection. Watermarking techniques can be classified based on various criteria, such as visibility, fragility, application domain, and extraction methods. However, their performance degrades against certain forgery attacks due to specific algorithmic loopholes. To deal with this issue, an in-depth performance evaluation and gap analysis of the latest invisible image watermarking schemes is presented in this paper. Then we propose an enhancement in terms of defining and using secret keys for the process of watermarking to improve temper detection and its localization against forgery attacks. First comprehensive testing is conducted regarding the imperceptibility and temper detection capability of these watermarking schemes against copypaste, object deletion, text addition, and collage attacks. This testing results in finding the security weaknesses against collage attacks. Finally, we implemented the proposed enhancement and illustrated improved results up-to 99% in terms of localizing forgery detection in certain scenarios of collage attacks enhancement. © 2024 IEEE.

关键词： image watermarking

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Real-Time Audio Analysis for Detection of Apnea Intervals in Health-Care System 10

Real-Time Audio Analysis for Detection of Apnea Intervals in...

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10th international conference on signal Processing and Intelligent systems, ICSPIS 2024

作者： Vahdani, Alireza Rahmati, Mahdieh Sharifi, Mehdi Ghatee, Mehdi Amirkabir University of Technology Iran Dep. Of Math. And Computer Science Tehran Iran

ISBN: (纸本)9798331532543

Accurate detection of respiratory pauses is crucial in patients undergoing cataract surgery under sedation, as undetected pauses can lead to serious complications. Conventional monitoring techniques, such as pulse oximetry and capnography, often fail to detect respiratory pauses in a timely manner, limiting their effectiveness in high-stakes clinical settings. To address this gap, we propose an automated method leveraging tracheal sound analysis using a deep learning model based on ResNet18. Our approach incorporates signal processing techniques, noise reduction, Mel-spectrogram extraction, and data augmentation, to optimize the model's performance. The model was evaluated using multiple performance metrics, including AUC of the precision-recall curve, F1 score, etc. It achieved an AUC of 92.5% and an F1 score of 89% for the apnea class, demonstrating the system's potential for precise, real-time respiratory monitoring in clinical environments. © 2024 IEEE.

关键词： image coding

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Real-time Monitoring and Early Warning of Cotton Diseases and Pests based on Agricultural internet of Things 4

Real-time Monitoring and Early Warning of Cotton Diseases an...

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4th international conference on Machine Learning and Big Data Analytics for IoT Security and Privacy, SPIoT 2023

作者： Cao, Bingyu Zhou, Peng Chen, Wei Wang, Huan Liu, Shengzhi School of Information Science and Technology Xinjiang College of Science & Technology Xinjiang Korla841000 China

During the entire process of cotton growth and development, diseases and pests occur frequently. Therefore, effective prevention and control of them has always been a difficult problem for agricultural scientists and cotton farmers. This article designed a real-time monitoring and early warning system for cotton pests and diseases. The data processing and analysis module can process and analyze the collected data, and uses technologies such as image recognition and data mining to achieve real-time recognition of cotton diseases and pests. Real-time monitoring of its operation was achieved through the mobile display and management module, enabling users to timely grasp the situation of cotton fields and take corresponding measures. This article compared the monitoring effectiveness and efficiency of traditional manual monitoring methods and agricultural internet of Things (IoT) based systems in the same farmland. The accuracy of traditional manual monitoring methods was 85%, while agricultural IoT systems showed higher accuracy, reaching 95%. Real-time monitoring and early warning of cotton diseases and pests based on the agricultural internet of Things is helpful for the prevention and control of flower diseases and pests and cotton field management. © 2024 The Authors.

关键词： Cotton

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Hybrid Approach to Enhance Security and Friendliness of Visual Secret Sharing Scheme 10

Hybrid Approach to Enhance Security and Friendliness of Visu...

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10th international conference on Emerging Trends in Engineering and technology - signal and Information Processing, ICETET-SIP-22 2022

作者： Panchbhai, Vishal V. Varade, Suchita W. Priyadarshini College of Engineering Department of Electronics & Telecommunication Engineering Nagpur India

ISBN: (纸本)9781665467414

In Block based Progressive Visual Secret Sharing (BPVSS) scheme, secret image will be recovered block by block from noise-like and meaningful shares. It is found that only meaningful shares are user-friendly. But the meaningful shares generated through BPVSS techniques show poor visual quality so it is suspicious to intruder while transmission over internet. Thus, this paper proposes a meaningful share generating strategy based on BPVSS and LSB-based steganography to solve the above-mentioned problem with less processing power and time. The proposed system delivers superior standard performance parameter readings and has various advantages, including (1) it is suited for grayscale as well as color secret images, (2) the contrast of the recovered image is the same as the original secret image, and (3) computational power and time required to recover secret image is very less. © 2022 IEEE.

关键词： Visualization Steganography image color analysis Information processing Gray-scale Market research Hybrid power systems

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Strong real-time transmission technology of lossless video based on Fibre Channel

Strong real-time transmission technology of lossless video b...

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2023 international conference on image, signal Processing, and Pattern Recognition, ISPP 2023

作者： Fu, Nian Tong, Wentao Zhao, Yupu Wuhan Digital Engineering Research Institute Hubei Wuhan430074 China

ISBN: (纸本)9781510666351

In order to meet the demand of strong real-time display in large integrated video network, this paper proposes a lossless video strong real-time transmission display model based on fibre channel protocol. By accessing the original video data of multi-standard sensors, the normalized video format is packaged into fibre channel protocol data for long-distance lossless transmission and real-time display. Finally, the multi-format original video transmission display based on fibre channel FC-AE-ASM protocol is realized by FPGA accelerating platform, this technology can support the real-time display of multi-format and multi-resolution raw data, the maximum bandwidth is no less than 130MB/S, the maximum resolution is no less than the 1920 times 1080@60HZ, and the total system delay is no more than 0.79ms. © 2023 SPIE.

关键词： image communication systems

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An internet of Things Assisted Autism Spectrum Disorder Prediction Using Deep Learning Principles with Medical image Processing Assistance

An Internet of Things Assisted Autism Spectrum Disorder Pred...

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2024 international conference on Innovative Computing, Intelligent Communication and Smart Electrical systems, ICSES 2024

作者： Bindhu, A. Mol, A. Sruthi Ravi, T. Bodkhe, R.G. Safori, Amjad Omar Vijayalakshmi, S. Infant Jesus College of Arts and Science for Women Department of Computer Science Mulagumoodu India Sathyabama Institute of Science and Technology Department of Ece Chennai India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India R.M.K. Engineering College Department of Electronics and Communication Engineering Chennai India Zaraa University Faculty of Media Department of Journalism and Digital Media Zaraa 13110 Jordan University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331543617

Characteristics of the complex neuro-developmental condition Autism Spectrum condition (ASD) include impaired social communication and restricted, repetitive patterns of behavior. This study presents a novel approach for identifying ASD in young infants by integrating deep learning, internet of Things (IoT) and medical image processing. The goal of this research is to create comprehensive and discriminative features by combining wearable sensor data with brain imaging and smart home environments. For the greatest results, early diagnosis is essential. The proposed method called IoT based Autism Predictive Framework (IoTAPF) has a high detection rate of 97.8% and is cross-validated against the current Learning based Autism Detection Strategy (LADS). The proposed approach aims at improving diagnostic specificity which helps in forecasting behaviors and thus pushing earlier interventions, providing better support for ASD individuals and their families. The paper discusses the design and implementation of the framework, including sensor integration, data management, and predictive modeling. Preliminary results from pilot studies are presented, demonstrating the framework's potential to improve diagnostic accuracy and support individualized care plans. The proposed solution represents a significant advancement in the application of IoT technology for early autism detection and management, paving the way for more proactive and data-driven approaches in the field of neuro-developmental disorders. © 2024 IEEE.

关键词： Diseases

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