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检索条件"任意字段=2024 Conference on Visual Communications and Image Processing"

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Compression and Transmission of 8K Stereoscopic VR Using VAE-GAN Latents and Standard Encoders

Compression and Transmission of 8K Stereoscopic VR Using VAE...

引用

IEEE conference on Wireless communications and Networking

作者： Sampreet Vaidya Hatem Abou-Zeid Diwakar Krishnamurthy Department of Electrical and Software Engineering University of Calgary Calgary AB Canada

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Despite the heightened popularity of Virtual Re-ality (VR), streaming high-resolution stereoscopic VR remains a challenge. This is primarily due to significant bandwidth demands of high-definition VR content. While advanced deep neural networks (DNN s) have demonstrated the potential to outperform standard codecs, their integration into real-world transmission frameworks is complex and not directly compatible with current encoding standards. To bridge this gap, this paper proposes a novel technique for compressing and transmitting 8K stereoscopic scenes using Variational Autoencoder (VAE) GAN latents represented as 3-channel RGB scenes that can be transmitted via standard encoders. The proposed method reduces bandwidth requirements by 45.1 % across different 8K scenes while maintaining visual quality, highlighting the effectiveness of the approach. This study also investigates the impact of varying patch-sizes of input frames for model training and evaluate its influence on client-side reconstructions. We then explore various transmission configurations of latent frames. Our findings suggest that while residual transmission offers limited benefits for 3-channel latent frame compression, raw transmission consistently yields better results, particularly for texture-heavy scenes. To the best of our knowledge, this is the first such transmission study on 8K stereoscopic scenes for cloud-based VR, providing valuable insights for optimizing high-resolution VR streaming systems. Code: ***/sampreetucalgary07/8K-VR-compression.

关键词： Training Cloud computing visualization Solid modeling Stereo image processing Bandwidth Virtual reality Streaming media Generative adversarial networks Standards

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SGCV-Net: An Efficient and Lightweight Semantic Segmentation Network for High-Resolution Remote Sensing images based on Semantic Guidance and CNN-ViT 24

SGCV-Net: An Efficient and Lightweight Semantic Segmentation...

引用

Proceedings of the 2024 International conference on image processing, Multimedia Technology and Maching Learning

作者： Dan Xie School of Computer Science Hubei University of Technology Wuhan Hubei China

ISBN: (纸本)9798400713880

The semantic segmentation technology can deeply analyze the land-coverage information in high-resolution remote sensing image (HRRSI), achieve accurate classifications and recognitions at the pixel level, and then provide a strong data support for various applications, such as urban planning, disaster management, and environmental monitoring. However, existing lightweight semantic segmentation networks often encounter challenges such as insufficient multi-scale feature extraction capabilities, blurred boundary contours, and a large number of parameters and computations. To address these issues, an efficient and lightweight semantic segmentation network based on the semantic guidance, the convolutional neural network (CNN), and the visual transformer (ViT), named SGCV-Net, was proposed. In the proposed SGCV-Net, an efficient and lightweight encoder with a novel depth-wise and partial convolution pair (DPP) module and a novel Sobel-enhanced MobileViT (SMViT) block was firstly designed to effectively extract semantic features. Then, a neck layer with a novel semantically guided data-dependent upsampling asymptotic feature pyramid network (DUs-AFPN) was designed for accurate multi-scale feature fusion. Finally, a minimal multilayer perceptron (MLP) layer was employed as the decoder. The proposed SGCV-Net was comparatively tested on the Gaofen image dataset. The experimental results showed that the SGCV-Net achieved the highest mean intersection over union (mIoU) with only 3.68 M parameters and 7.61 GFLOPs and the mIoU reached 83.16%.

关键词： High-resolution remote sensing image

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2nd International conference on Information Technologies and Their Applications, ITTA 2024

2nd International Conference on Information Technologies and...

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2nd International conference on Information Technologies and Their Applications, ITTA 2024

ISBN: (纸本)9783031734168

The proceedings contain 60 papers. The special focus in this conference is on Information Technologies and Their Applications. The topics include: Turkish Legal Single-Document Summarizing;technologies and Systems for Monitoring the Onset of Accidents at Strategic Construction;optimizing Feature Distributions for Unsupervised Deep Learning-Based Fabric Defect Detection and Localization;conservation of Wildlife in Hunting Tourism Using Artificial Intelligence and image processing in Smart Firearms;optimizing Microarray Gene Selection in Colon Cancer: An Enhanced Metaheuristic Algorithm for Feature Selection;securing Vehicular Ad-Hoc Networks: A Blockchain and Software Defined Network Approach for Enhanced Efficiency, Safety, and Security;influence of Process Noise Biases to Satellite Attitude Filters’ Estimates;information-Retrieval System of Rock Pictures of Different Countries and Continents;convergence of Sharpness-Aware Minimization with Momentum;vulnerability Assessment and Penetration Testing of University Network;preliminary Orbit Estimation for Turksat 5B via Extended and Unscented Kalman Filtering;Three-Axis Attitude Determination of a Nanosatellite Using Optimized TRIAD Algorithms;computer System of Evaluation of the Mass Exam Results Based on Recognition of Handprinted Azerbaijani Characters;Empirical Analysis of Conformer Impact on the CTC-CRF Model in Kazakh Speech Recognition;ontological Approach to Describing the Interaction of Information Processes in Intelligent Systems;LANet: Lightweight Attention Network for Medical image Segmentation;Lightweight Hybrid CNN Model for Face Presentation Attack Detection;disaggregation Analyses of the External/Internal Parameters for Chloride Diffusion in Concrete Structures: Artificial Intelligence by k-means Algorithm;k-means for Small Earthquakes. Alternative Disaggregation Analyses by Considering Wave Components and Soil Types;algorithm for Distributing Incoming Messages Among Handlers.

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2nd International conference on Information Technologies and Their Applications, ITTA 2024

2nd International Conference on Information Technologies and...

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2nd International conference on Information Technologies and Their Applications, ITTA 2024

ISBN: (纸本)9783031734199

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High-Resolution 3D Reconstruction Based on Prior Knowledge with Binocular Structured Light 8th

High-Resolution 3D Reconstruction Based on Prior Knowledge w...

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8th International conference on Life System Modeling and Simulation, LSMS 2024 and 8th International conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2024

作者： Luo, Desen Zhou, Wenju Liang, Wei Jin, Maoyu Ren, Xinzhen Chen, Rongfei Han, Xiaofei Shanghai Key Laboratory of Power Station Automation Technology School of Mechatronic Engineering and Automation Shanghai University Shanghai200444 China School of Electronic and Electrical Engineering University of Leeds LeedsLS2 9JT United Kingdom

ISBN: (纸本)9789819602933

The 3D reconstruction process using binocular structured light goes through two streak image processing: wrapped phase solving and phase unfolding. At least 3 images are required to solve the parcel phase using the phase shift method, and acquiring more than one image will affect the efficiency of the reconstruction. In this paper, a neural network model is designed to solve for the wrapped phase using a single high-resolution phase shift image. Firstly, highly accurate wrapped phase results are solved using 12-step phase images and allowed to be used as theoretical reference values for model training. Secondly, using the optimized U-Net end-to-end generation framework, a single phase-shifted image is input, and a two-channel floating-point image of the calculated wrapped phase numerator and the denominator is output. Thirdly, the output two-channel floating-point image is processed with the arctan function to obtain the wrapped phase of the inference output. In the inference phase, the wrapped phase can be solved using a single image using the a priori knowledge learned from the model. The efficiency of structured light 3D reconstruction can be improved by utilizing the high-resolution wrapped phase generation framework proposed in this paper. Compared with the 12-step PS method, the overall reconstruction time is reduced by 87.85%, and the reconstruction accuracy is close to that of the 12-step PS method. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： image reconstruction

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Composite Awareness-Based Knowledge Distillation for Medical Anomaly Detection 1st

Composite Awareness-Based Knowledge Distillation for Medical...

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1st International conference on Pattern Analysis and Machine Intelligence, ICPAMI 2024

作者： Dong, Shiwen Ding, Xiaowei Department of Electronic Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9789819633487

In the field of medical image analysis, supervised deep learning methods have become mainstream but still face significant challenges. Firstly, they require substantial annotated data, which is costly and time-consuming to obtain. Secondly, there is a class imbalance issue, especially with rare diseases that have limited images and diverse lesion types, making accurate modeling difficult. To address these issues, we propose an anomaly detection model based on knowledge distillation. Our approach leverages the differences in intermediate layer features between a pre-trained teacher network and a student network trained on normal images through knowledge distillation to detect anomalies. We incorporate a composite awareness module to learn both local regional and global spatial information effectively. Experiments conducted on three medical datasets prove our approach performs competitive anomaly detection results compared to existing methods for medical images. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Medical image processing

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Stock Investor Sentiment Analysis Based on NLP 1st

Stock Investor Sentiment Analysis Based on NLP

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1st International conference on Pattern Analysis and Machine Intelligence, ICPAMI 2024

作者： Liang, Jingxin Deng, Chao Yang, Jie Shanghai Normal University Tianhua College AI School Shanghai China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9789819633487

This study investigates the reciprocal relationship between investor sentiment and capital market dynamics, leveraging advanced techniques in data extraction and analysis. By employing web crawling and natural language processing (NLP), we gather and categorize investor comments from stock forums pertinent to the Shanghai and Shenzhen Indices. Utilizing Support Vector Machines and Logistic Regression within an NLP framework, we develop an automated system that transforms comments into sentiment indicators. These indicators are further quantified into daily sentiment indices following denoising procedures to emphasize persistent market trends. Comparative assessments reveal strong correlations between refined sentiment indices and actual stock index movements, confirming their predictive power for market fluctuations. The study offers investors a sentiment-analytics tool, augmenting the multi-faceted approach to investment strategy formulation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Web crawler

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1st International Research conference on Computing Technologies for Sustainable Development, IRCCTSD 2024

1st International Research Conference on Computing Technolog...

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1st International Research conference on Computing Technologies for Sustainable Development, IRCCTSD 2024

ISBN: (纸本)9783031823824

The proceedings contain 82 papers. The special focus in this conference is on Computing Technologies for Sustainable Development. The topics include: Towards Scalable and Cost-Effective Design for Intrusion Detection for IIoT Environment Using Metric Active Learning;an Interactive Dashboard for Investigating Water Potability in Telangana State;efficient Multi-operand Binary Tree Adder with Minimal Area Delay and Energy Consumption;developing Simulink Model of Microgrid Energy Management System and Optimizing Electricity Cost Using Linear Optimization Approach;advancing Smart Water Management: Integrating Invasive and Non-invasive IoT Sensors for Sustainable Hot and Cold-Water Usage Monitoring;atmospheric River Detection - A Survey on Deep Learning and Quantum Neural Networks;emotion Recognition Using Text in a Chatbot Using Logistic Regression;Sign Language Recognition System for Seamless Human-AI Interaction;Indian Sign Language Gesture Recognition Using Bi-Directional LSTM and GRU with Text to Speech Conversion;comprehensive Exploration of Facial Emotion Recognition Using Conventional Machine Learning and Transfer Learning Models;type Sculpt: Text-to-3D Generation with Personalized Precision Using Adaptive Attention Mechanism;carnatic Raga Recognition Using Deep Learning Techniques;visual Verse 2.0: image Generation with image Inpaiting;Data Brain: Streamlining Data Science with AI-Assisted Conversations;semantic Segmentation of Satellite imagery Using Optimized U-Net Model;video Caption Generation Using Deep Learning;leveraging LangChain Framework and Large Language Models for Conversational Chatbot Development;Designing of In-Memory Computing SRAM Energy-Efficient for Artificial Intelligence;integrating Students as Academic Partners in Software Engineering: A Group Software Development Case Study;chattitude in Education: Chatbot Expertise for Educational System;PDFQA: A PDF Question Answering System.

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1st International Research conference on Computing Technologies for Sustainable Development, IRCCTSD 2024

1st International Research Conference on Computing Technolog...

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1st International Research conference on Computing Technologies for Sustainable Development, IRCCTSD 2024

ISBN: (纸本)9783031823855

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