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检索条件"任意字段=Conference on Visual Communications and Image Processing 2003"

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Memory-Efficient Point Cloud Registration via Overlapping Region Sampling

Memory-Efficient Point Cloud Registration via Overlapping Re...

引用

IEEE visual communications and image processing (VCIP)

作者： Tomoyasu Shimada Kazuhiko Murasaki Shogo Sato Toshihiko Nishimura Taiga Yoshida Ryuichi Tanida NTT Corporation Japan

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Recent advances in deep learning have improved 3D point cloud registration but increased graphics processing unit (GPU) memory usage, often requiring preliminary sampling that reduces accuracy. We propose an overlapping region sampling method to reduce memory usage while maintaining accuracy. Our approach estimates the overlapping region and intensively samples from it, using a k-nearest-neighbor (kNN) based point compression mechanism with multi layer perceptron (MLP) and transformer architectures. Evaluations on 3DMatch and 3DLoMatch datasets show our method outperforms other sampling methods in registration recall, especially at lower GPU memory levels. For 3DMatch, we achieve 94% recall with 33% reduced memory usage, with greater advantages in 3DLoMatch. Our method enables efficient large-scale point cloud registration in resource-constrained environments, maintaining high accuracy while significantly reducing memory requirements.

关键词： Point cloud compression Three-dimensional displays Accuracy image coding visual communication Memory management Graphics processing units Sampling methods Transformers Hardware

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Learning-based Lossless Event Data Compression

Learning-based Lossless Event Data Compression

引用

IEEE visual communications and image processing (VCIP)

作者： Ahmadreza Sezavar Catarina Brites João Ascenso Instituto Superior Técnico Instituto de Telecomunicações Instituto Universitário de Lisboa (ISCTE-IUL) Instituto de Telecomunicações

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Emerging event cameras acquire visual information by detecting time domain brightness changes asynchronously at the pixel level and, unlike conventional cameras, are able to provide high temporal resolution, very high dynamic range, low latency, and low power consumption. Considering the huge amount of data involved, efficient compression solutions are very much needed. In this context, this paper presents a novel deep-learning-based lossless event data compression scheme based on octree partitioning and a learned hyperprior model. The proposed method arranges the event stream as a 3D volume and employs an octree structure for adaptive partitioning. A deep neural network-based entropy model, using a hyperprior, is then applied. Experimental results demonstrate that the proposed method outperforms traditional lossless data compression techniques in terms of compression ratio and bits per event.

关键词： Adaptation models visualization image coding visual communication Octrees Data compression Cameras Entropy Data models Time-domain analysis

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Warm-start NeRF: Accelerating Per-scene Training of NeRF-based Light-Field Representation

Warm-start NeRF: Accelerating Per-scene Training of NeRF-bas...

引用

IEEE visual communications and image processing (VCIP)

作者： Takuto Nishio Chihiro Tsutake Keita Takahashi Toshiaki Fujii Nagoya University Japan

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

A light field is represented as a set of multi-view images captured from a dense 2-D array of viewpoints. To treat a light field as being continuous, we represent it as a neural radiance field (NeRF), which is a learned representation of a 3-D scene. NeRFs are renowned for their ability to reconstruct a target 3-D scene with compelling visual quality, but they are slow to train. A solution for this problem is to use a tiny neural network and trainable volumetric features as the scene representation, which is considered the baseline of our research. For further acceleration, we propose a method for warm-starting the per-scene training by setting good initial values for the trainable parameters. To this end, we introduce another encoder network to obtain the initial volumetric features from the target light field. Starting with the appropriate initial values, our method can achieve better rendering quality with fewer training iterations than the baseline.

关键词： Training visualization Three-dimensional displays visual communication Neural networks Neural radiance field Rendering (computer graphics) Light fields image reconstruction

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Low-Rate Feature Compression for Humans and Machines with Dual Aggregation Attention

Low-Rate Feature Compression for Humans and Machines with Du...

引用

IEEE visual communications and image processing (VCIP)

作者： Ruixi Ma Ping An Shipei Wang Xinpeng Huang Chao Yang School of Communication and Information Engineering Shanghai University China

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

The Collaborative Intelligence (CI) framework offers innovative approaches for deploying Deep Neural Networks (DNNs). However, the limitations of communication resources require minimizing the transmission of bits between the edge and cloud devices to meet the requirements of both machine recognition and human perception. Previous research has demonstrated that the transmission of intermediate layer features of the vision backbone can achieve superior performance in machine vision tasks at very low bit rates without consuming additional bits. Nonetheless, the reduced bit rate poses challenges for image reconstruction. We propose a CI framework that compresses the intermediate features of the Swin Transformer and utilizes a Feature Recovery Module (FRM) to restore crucial information for image reconstruction, thereby satisfying both machine and human visual tasks at low bit rates. Additionally, we introduce a Residual Dual-attention Aggregation Block (RDAB) that exploits both local and global information for effective compression and reconstruction. We conducted experiments on the CUB_200_2011 dataset. The results demonstrate that the proposed method delivers superior performance at low-rate scenarios.

关键词： Performance evaluation visualization image coding visual communication Machine vision Bit rate Transformers image restoration image reconstruction Collaborative intelligence

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ReLI-QA: A Multidimensional Quality Assessment Dataset for Relighted Human Heads

ReLI-QA: A Multidimensional Quality Assessment Dataset for R...

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IEEE visual communications and image processing (VCIP)

作者： Yingjie Zhou Zicheng Zhang Farong Wen Jun Jia Xiongkuo Min Jia Wang Guangtao Zhai Institute of Image Communication and Network Engineering Shanghai Jiao Tong University China PengCheng Laboratory China

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Lighting conditions significantly affect the quality of both real and AI-generated images. Facial images are particularly sensitive to lighting due to their detailed nature and the importance of facial features in conveying identity. Poor lighting can easily obscure these critical details. To address this issue, various portrait relighting methods have been developed to adjust the lighting in improperly exposed images. However, these methods often encounter challenges such as overexposure, underexposure, and detail loss in the relighted portraits. Consequently, there is a need for effective quality assessment and control of relighted human heads (RHHs). In this study, one proposed simple baseline and three typical relighting methods are applied to six selected human head (HH) images, resulting in the creation of a quality assessment dataset named ReLI-QA, which comprises 840 RHHs. A multidimensional subjective quality assessment method based on visual guidance is proposed to accurately evaluate the visual quality of each RHH in the dataset. By analyzing the results of subjective experiments, the quality of RHHs is shown to be affected by multiple factors. Finally, based on ReLI-QA, some typical image quality assessment (IQA) methods are selected for benchmark experiments. The experimental results show the limitations of the existing methods in RHH quality assessment. The dataset and code for this research has been released at https://***/zyj-2000/ReLI-QA.

关键词： image quality visualization Head visual communication image processing Lighting Benchmark testing Quality assessment Research and development Facial features

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4th International conference on Applied Technologies, ICAT 2022

4th International Conference on Applied Technologies, ICAT 2...

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4th International conference on Applied Technologies, ICAT 2022

ISBN: (纸本)9783031249709

The proceedings contain 113 papers. The special focus in this conference is on Applied Technologies. The topics include: Automatic Evaluation of Physiotherapy Activities Using Deep Learning Techniques;multivariable Control Approach Applied to the Embryo Incubation Process of Gallus Gallus Domesticus;discovering visual Deficiencies in Pilots Candidates Using Data Mining;an Electronic Equipment for Measuring Color Difference Between Tissues Based on Digital image processing and Neural Networks;artificial Intelligence-Based Banana Ripeness Detection;U-Net vs. TransUNet: Performance Comparison in Medical image Segmentation;access Control Through Mask Detection and Estimation of People Capacity in Covered Premises;customer Segmentation in Food Retail Sector: An Approach from Customer Behavior and Product Association Rules;Bibliometric Analysis of Web of Science Database STEM Fields in Engineering and Mathematics. Ecuador’s Case Study;smart Antenna Array for Optimal Electromagnetic Energy Capture;image processing Method to Estimate Water Quality Parameter;use of the Student Engagement as a Strategy to Optimize Online Education, Applying a Supervised Machine Learning Model Using Facial Recognition;crime Data Analysis Using Machine Learning Models;Gene Therapy as a Solution to Genetic Diseases Through DNA Manipulation;Vulnerability of CAPTCHA Systems Using Bots with Computer Vision Abilities;military Leadership in the Ecuadorian Army;analysis of Key Variables for Ecuadorian Defense Industry Development;the Prioritization of External Security as a Means of Guaranteeing Multidimensional Security and Economic Growth;Comparative Study of Deep Learning Algorithms in the Detection of Phishing Attacks Based on HTML and Text Obtained from Web Pages;ground Robot for Search and Rescue Management;virtual Training Module for the Production of Rubber Adhesives Through the Production of Cyclopentenol.

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Cross-Device image Saliency Detection: Database and Comparative Analysis

Cross-Device Image Saliency Detection: Database and Comparat...

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IEEE visual communications and image processing (VCIP)

作者： Xiaoying Ding Guanghui Yue Yingxue Zhang School of Information Engineering Zhongnan University of Economics and Law Wuhan China School of Biomedical Engineering Shenzhen University Medical School Shenzhen University Shenzhen China College of Artificial Intelligence Tianjin University of Science and Technology Tianjin China

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

While saliency detection for images has been extensively studied during the past decades, only a little work explores the influence of different viewing devices (i.e., tablet computer, mobile phone) towards human visual attention behavior. The lack of research in this field hinders the research progress in cross-device image saliency detection. In this paper, we first establish a novel cross-device saliency detection (CDSD) database based on eye-tracking experiments and investigate subjects’ visual attention behavior when using different viewing devices. Then, we evaluate several classic saliency detection models using the CDSD database and the evaluation results indicate that the cross-device performance of these models need further improvement. Finally, some meaningful discussions are provided which might enlighten the design of cross-device saliency detection model. The proposed CDSD database will be made publicly available.

关键词： Performance evaluation visualization visual communication Tablet computers Gaze tracking Mobile handsets Stability analysis Behavioral sciences visual databases Saliency detection

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4th International conference on Applied Technologies, ICAT 2022

4th International Conference on Applied Technologies, ICAT 2...

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4th International conference on Applied Technologies, ICAT 2022

ISBN: (纸本)9783031249778

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4th International conference on Applied Technologies, ICAT 2022

4th International Conference on Applied Technologies, ICAT 2...

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4th International conference on Applied Technologies, ICAT 2022

ISBN: (纸本)9783031249846

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Perceptual Skin Tone Color Difference Measurement for Portrait Photography

Perceptual Skin Tone Color Difference Measurement for Portra...

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IEEE visual communications and image processing (VCIP)

作者： Shiqi Gao Huiyu Duan Qihang Xu Jia Wang Xiongkuo Min Guangtao Zhai Patrick Le Callet

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

In portrait photography, measuring the perceptual color differences (CDs) of skin tone is significant. Many studies have documented that the perception of skin tone is characteristically different from that of other colors. However, most existing CD measures are proposed based on psychophysical data of uniform color patches or natural images, and do not generalize well to the measurement of skin tone. In this paper, we construct the first large-scale portrait dataset for perceptual skin tone CD assessment and conduct psychophysical experiments to collect 160,000 perceptual CD judgments for 40,000 image triplets. Based on this dataset, we propose a deep skin tone CD measure for portrait photography. Extensive experiments demonstrate that our measure substantially outperforms existing CD measures on the problem of assessing skin tone CDs. The constructed dataset and code will be released to facilitate future research.

关键词： Photography Codes image color analysis visual communication Network architecture Skin Psychophysics

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