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检索条件"任意字段=Conference on Visual Communications and Image Processing 2002"

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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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3rd International conference on Electronic Governance with Emerging Technologies, EGETC 2024

3rd International Conference on Electronic Governance with E...

引用

3rd International conference on Electronic Governance with Emerging Technologies, EGETC 2024

ISBN: (纸本)9783031770289

The proceedings contain 17 papers. The special focus in this conference is on Electronic Governance with Emerging Technologies. The topics include: Ecological Development of a Novel Machine Learning Technique for Electricity Consumption and Cost Prediction in Cloud Computing;blockchain Based Decentralized Approach for Securing Fog Computing Environment;Enhancing Face Spoofing Detection Via CNN Model Integration with Normalized Features;Study of ZigBee-Based Communication to Share Operative Weather Information Between UASs in the Frame of Smart Cities Concept;automating Exam Assessment Through Digital Transformation and image processing;an Analysis of Ethical Dimension of Machine Learning Models in Software Defect Prediction in Healthcare Sector;leaf Diseases Detection in Peach Using EfficientNet;medInsight: A Personalized Prescription Recommendation Using Machine Learning;intelligent Machine Learning for Cybersecurity: Anomaly Detection in Network Intrusion Systems and Beyond;Enhancing Cyber Resilience: Implementing DFMCAI for Threat Detection and Analysis;forecasting Future Credit Card Delinquencies: Leveraging Data Tables and visual Analysis for Enhanced Accuracy;navigating the Strategic Landscape: An In-Depth Review of the Strategic Planning Process;exploring Text Vectorization on the Polarity Detection of Spanish Comments;Evolutionary Optimization Techniques for the Design of Three-Dimensional UAV’s Antenna Arrays;cooperative Multi-agent Pursuit-Evasion Using Convolutional Neural Networks.

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Accelerating Traditional Object Detection Methods on Sophisticated Embedded Systems 4th

Accelerating Traditional Object Detection Methods on Sophis...

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4th International conference on Intelligent Systems and Pattern Recognition, ISPR 2024

作者： Sehairi, Kamal Bouhafs, Abdelkader Boucherit, Ibtissam Mama Bouwmans, Thierry Chouireb, Fatima Department of Physics Laboratory of Applied Sciences and Didactics École Normale Supérieure de Laghouat Laghouat Algeria Department of Electronics Telecommunication Signals and Systems Laboratory University Amar Telidji of Laghouat Laghouat Algeria Department of Computer Science Lab. Mathématiques Image et Applications Université de La Rochelle La Rochelle France

ISBN: (纸本)9783031821523

Identifying and locating objects in images and videos, including elements like traffic signs, vehicles, buildings, and people, constitutes a fundamental and demanding task in computer vision, known as object detection. Due to the higher computing complexity of this technique and the large amount of data carried by the video signal, it is nearly impossible for ordinary general-purpose processors GPPs or CPUs to run these techniques in real-time, especially for embedded systems applications. Therefore, special hardware that can acquire, control, or execute in parallel is required. These specialized hardware systems include Digital Signal Processors DSPs, Field Programmable Gate Arrays FPGAs, visual processing Units VPUs, Tensor processing Units TPUs, Neural processing Units NPUs or Graphics processing Units GPUs. This work presents the benefits of accelerating traditional object detection methods on a high-end embedded system, the Jetson Nano Developer Kit. This single computer board is equipped with the Tegra K1 System on Chip SoC, which is composed of a quad-core ARM A15 and 192 cores of Kepler-embedded GPU. Computing acceleration was ensured via the use of the CUDA OpenCV library for both the Histogram of Oriented Gradients HOG and the Haar Cascade Classifier. For VGA resolution, results reveal that the GPU implementation on this embedded system is 1.4× faster than the CPU for the HOG method and 2× for the Haar Cascade Classifier method. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： System-on-chip

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MKD-YOLO: Multi-Scale and Knowledge-Distilling YOLO for Efficient PPE Compliance Detection

MKD-YOLO: Multi-Scale and Knowledge-Distilling YOLO for Effi...

引用

International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Juntao Zan Yang Fang Qilie Liu Uswah Khairuddin Yan Li Kaiwei Sun School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Chongqing China Chongqing Key Laboratory of Image Cognition Chongqing University of Posts and Telecommunications Chongqing China Malaysia-Japan International Institute of Technology University of Technology Malaysia Kuala Lumpur Malaysia Department of Electrical and Computer Engineering Inha University Incheon South Korea Key Laboratory of Data Engineering and Visual Computing Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

YOLO-based models are widely used for personal protective equipment (PPE) compliance detection due to their excellent detection performance and efficiency. However, most YOLO models are not competent for detection tasks in complex industrial scenarios such as remote surveillance and extremely small targets. In addition, there is a lack of effective model lightweighting and knowledge transfer approaches for industrial deployment. To this end, this paper proposes a Multi-scale and Knowledge-Distilling YOLO (MKD-YOLO) based on YOLOv8n for efficient PPE compliance detection. Specifically, in backbone stage, we design an Efficient Multi-Scale Enhanced Convolution (C2f-EMSEC) module and Large Spatial Pyramid Pooling-Fast (LSPPF) module for multi-scale and global-contextual feature learning as well as reducing model complexity. Then, in neck stage, a refined Bidirectional feature Pyramid Network (BPNet) is designated to capture fine-grained details for extremely small object detection. Moreover, we apply channel-wise knowledge distillation to facilitate model lightweighting and domain-specific knowledge transfer learning. Experiments on our proposed dataset and public datasets show that the proposed MKD-YOLO achieves a new state-of-the-art (SOTA) detection performance and efficiency for practical PPE compliance detection tasks. Codes and the dataset are available at https://***/z1Zjt/MKD-YOLO.

关键词： Personal protective equipment YOLO Representation learning Convolution Surveillance Speech enhancement Feature extraction Neck Kernel Knowledge transfer

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visual communications and image processing 2002 : 21-23 January 2002, San Jose, USA /

Visual communications and image processing 2002 : 21-23 Janu...

引用

2002年

作者： C.-C. Jay Kuo chair/editor sponsored by IS&T--the Society for Imaging Science and Technology SPI

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A 3D MRI Brain image Segmentation and Reconstruction System Based on Augmented Reality Technology 10th

A 3D MRI Brain Image Segmentation and Reconstruction System ...

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10th China Health Information processing conference, CHIP 2024

作者： Zhao, Wang Lu, Peixin Hu, LianTing Lu, Long School of Management Engineering Henan University of Engineering Zhengzhou China School of Safety Science and Emergency Management Wuhan University of Technology Wuhan China Guangdong Provincial People’s Hospital Guangzhou China School of Information Management Wuhan University Wuhan China The Center for Healthcare Big Data Research The Big Data Institute Wuhan University Wuhan China Institute of Pediatrics Guangzhou Women and Children’s Medical Center Guangzhou Medical University Guangzhou China School of Public Health Wuhan University Wuhan China

ISBN: (纸本)9789819637546

Background With the rapid development of information technology and the digitization of medical devices, various diseases require the use of medical imaging equipment for diagnosis. At present, various medical imaging diagnostic equipment such as CT and nuclear magnetic resonance can provide two-dimensional planar images of diseases. Doctors urgently need to accurately determine the spatial location, size, geometry, and spatial relationship with the surrounding tissue. Therefore, it is very important to use computer technology to segment 3D MRI images, determine the location of lesions, and then perform 3D reconstruction. Method At present, automatic recognition and marking of brain images are displayed in two dimensions. Therefore, it is necessary to use 3D visualization technology for reconstruction. In addition, it can be combined with virtual and real, and some additional information is superimposed on the brain image for integrated display. In addition, a combination of virtual and real needs to be superimposed, and some additional information is superimposed on the brain image for integrated display. The research focus of this paper includes two main parts: disease segmentation and 3D reconstruction visualization. Firstly, the disease segmentation method based on 3D MRI brain image files was designed, and then the feature extraction and 3D reconstruction functions were designed. Thereby forming a complete process of disease region segmentation and three-dimensional reconstruction. Results This study is based on a three-dimensional MRI brain image segmentation algorithm. The algorithm is advanced in technology, high in accuracy, and can effectively identify the location of the disease. Then, this study used the Unity tool to implement a three-dimensional reconstruction and visual display program for brain image disease segmentation. Therefore, the doctor can quickly and intuitively grasp the spatial information inside the brain and the information of the lesion

关键词： image segmentation

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Resolution enhancement through TARID processing

Resolution enhancement through TARID processing

引用

conference on visual communications and image processing 2002

作者： Schuler, JM Howard, JG Warren, PR Scribner, DA USN Res Lab Div Opt Sci Washington DC 20375 USA

ISBN: (纸本)0819444111

This paper outlines a generalized image reconstruction approach to improve the resolution of an Electro-Optic (EO) imaging sensor using multiple frames of an image sequence. This method only assumes the constituent video has some ambient motion between the sensor and stationary background, and the optical image is physically captured by a staring focal plane array.

关键词： Video Staring arrays image sensors Composites image processing Motion estimation Sensors Distortion image resolution Optical flow

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Robust tie-point registration of disparate imaging sensors by matching optical flow

Robust tie-point registration of disparate imaging sensors b...

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conference on visual communications and image processing 2002

作者： Schuler, JM Howard, JG Warren, PR Scribner, DA USN Res Lab Washington DC 20375 USA

ISBN: (纸本)0819444111

Many imaging systems consist of a combination of distinct electro-optic sensors that are constrained to view the same scene through a common aperture or from a common platform. Oftentimes, a spatial registration of one sensor's image is required to conform to the slightly disparate imaging geometry of a different sensor on the same platform. This is generally achieved through a judicious selection of image tie-points and geometric transformation model. This paper outlines a procedure to improve any such registration technique by leveraging the temporal motion within a pair of video sequences and requiring an additional constraint of minimizing the disparity in optical flow between registered video sequences,.

关键词： image registration Video Optical flow Sensors image fusion image processing Optical sensors image sensors Video processing Visible radiation

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An embedded image coding algorithm using set partitioning in block trees of wavelet coefficients

An embedded image coding algorithm using set partitioning in...

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conference on visual communications and image processing 2002

作者： Yang, CM Ho, YS Kwangju Inst Sci & Technol Dept Informat & Commun Visual Commun Lab Puk Gu Kwangju 500712 South Korea

ISBN: (纸本)0819444111

In this paper, we propose a simple but efficient wavelet-based embedded image coder that employs a new inter-band magnitude relationship in the wavelet coefficients and block trees. The proposed scheme includes multi-level dyadic wavelet decomposition, raster scanning within each subband, formation of block trees, partitioning of block trees and adaptive arithmetic entropy coding. Although the proposed scheme is simple, it produces a bitstream with a rich set of features, including SNR scalability and the embedded nature. Experimental results demonstrate that the new scheme is quite competitive to and outperforms other good image coders in the literature.

关键词： wavelet transform image coding block tree block partitioning

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On packetization of JPEG2000 codestreams

On packetization of JPEG2000 codestreams

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conference on visual communications and image processing 2002

作者： Bilgin, A Wu, ZY Marcellin, MW Univ Arizona Dept Elect & Comp Engn Tucson AZ 85721 USA

ISBN: (纸本)0819444111

We introduce techniques that improve the error resilience of JPEG2000 against packet losses. The presented methods operate on JPEG2000 codestreams and consider the properties of different codestream segments. Experiments indicate that error resilience of JPEG2000 codestreams against packet losses can be improved significantly using these techniques. The performance of the proposed methods compares favorably with existing algorithms.

关键词： image communication systems

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