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检索条件"任意字段=18th Chinese Conference on Image and Graphics Technology and Application Conference, IGTA 2023"

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18th chinese conference on image and graphics technology and application conference, igta 2023

18th Chinese Conference on Image and Graphics Technology and...

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18th chinese conference on image and graphics technology and application conference, igta 2023

ISBN: (纸本)9789819975488

the proceedings contain 35 papers. the special focus in this conference is on image and graphics technology and application. the topics include: Infrared Small Target Detection Based on Prior Weighed Sparse Decomposition;CPML: Category Probability Mask Learning for Fine-Grained Visual Classification;DPFMN: Dual-Path Feature Match Network for RGB-D and RGB-T Salient Object Detection;a Optical Flow-Based Fight Behavior Detection Method for Campus Scene;attention-Guided Neural Network for Face Mask Detection;double-Stream Network for Clothes-Changing Person Re-identification Based on Clothes Related Feature Suppression and Attention Mechanism;semantic Guided Attention for Weakly Supervised Group Activity Recognition;research on Contrast Calculation Method for Color image;a Position-Based Dynamics Simulation of Liver Deformation with Ellipsoidal Particles;a Self-supervised Learning Reconstruction Algorithm with an Encoder-Decoder Architecture for Diffuse Optical Tomography;a Perceptually Uniform Gloss Space for Translucent Materials;x-ray Computed Tomography Reconstruction Algorithm for Refractive Index Gradient;Global Temperature Prediction Models Based on ARIMA and LSTM;real-Time image Stitching with Transformers for Complex Traffic Environment;research on 3D Visual Perception Quality Metric Based on the Principle of Light Field image Display;nonlinearity Affection Analysis of Spectral Information Reconstruction by Trichromatic Imaging System;adversarial Reinforcement Learning for Steering Cars from Virtual to Real World;efficient Multimodal-Contribution-Aware N-pair Network for Focal Liver Lesions;Lung Nodule Classification Based on SE-ResNet152 and Stratified Sampling;Fusing CNN and Transformer for Diabetic Retinopathy image Grading;TSR-Net: A Two-Step Reconstruction Approach for Cherenkov-Excited Luminescence Scanned Tomography;Cross-modal Domain Adaptive Instance Segmentation in SAR images via Instance-aware Adaptation;ME-GraphSAGE: Minority Class Feature Enhanced

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PromptIQA: Boosting the Performance and Generalization for No-Reference image Quality Assessment via Prompts 18th

PromptIQA: Boosting the Performance and Generalization for N...

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18th European conference on Computer Vision (ECCV)

作者： Chen, Zewen Qin, Haina Wang, Juan Yuan, Chunfeng Li, Bing Hu, Weiming Wang, Liang CASIA State Key Lab Multimodal Artificial Intelligence Beijing Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China PeopleAI Inc Beijing Peoples R China ShanghaiTech Univ Sch Informat Sci & Technol Shanghai Peoples R China OPPO Hardware Syst Beijing Peoples R China

ISBN: (纸本)9783031732317;9783031732324

Due to the diversity of assessment requirements in various application scenarios for the IQA task, existing IQA methods struggle to directly adapt to these varied requirements after training. thus, when facing new requirements, a typical approach is fine-tuning these models on datasets specifically created for those requirements. However, it is time-consuming to establish IQA datasets. In this work, we propose a Prompt-based IQA (PromptIQA) that can fast adapt to new requirements without fine-tuning after training. On one hand, it utilizes a short sequence of image-Score Pairs (ISP) as prompts for targeted predictions, which significantly reduces the dependency on the data requirements. On the other hand, PromptIQA is trained on a mixed dataset with two proposed data augmentation strategies to learn diverse requirements, thus enabling it to fast adapt to new requirements. Experiments indicate that the PromptIQA outperforms SOTA methods with higher performance and better generalization. the code is available at the link.

关键词： NR-IQA image-Score Pairs Assessment Requirement

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A Federated Learning Framework for Lightweight Model Contrast for Finger Vein Recognition 18th

A Federated Learning Framework for Lightweight Model Contra...

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18th chinese conference on Biometric Recognition, CCBR 2024

作者： Chen, Guang Xie, Tianming Yang, Xu Tian, Feng Kang, Wenxiong School of Automation Science and Engineering South China University of Technology Guangzhou510641 China GRGBanking Equipment Co. Ltd. Guangzhou510663 China

ISBN: (纸本)9789819610679

the training efficacy of finger vein models is influenced by varied device image acquisition characteristics, collector gestures, and contact modes. However, conventional approaches relying on vast data volumes face challenges in the finger vein domain due to stringent security and privacy concerns, limiting data availability. this study proposes a federal learning framework for vein recognition model training, introducing FedFvMCo, a model of contrastive learning among participants. By comparing global and local lightweight model parameters under data convergence limitations, FedFvMCo enhances cross-domain finger vein recognition performance and model generalization. Incorporating an attention mechanism into the lightweight model further optimizes its application in resource-constrained intelligent devices. this framework is validated using four public datasets, demonstrating improved model performance and independence from data convergence constraints. this method can be extended to different modes of biometrics recognition. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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BM3D-UGanNet: A Hybrid Deep Learning Network for Low-Light thangka image Enhancement 19th

BM3D-UGanNet: A Hybrid Deep Learning Network for Low-Light ...

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19th chinese conference on image and graphics Technologies and applications, igta 2024

作者： Peng, Quanhong Zhang, Dan Zhou, Mingquan Zhang, Jianpeng Song, Meihua Wang, Ning Xu, Chenhao Computer College of Qinghai Normal University Xining81017 China Xining81017 China

ISBN: (纸本)9789819799183

thangka is a Tibetan traditional painting art known for its intricate textures and profound cultural significance. Typically displayed in low-light environments in museums to prevent light from affecting its quality, this also makes it difficult for visitors to capture details and is not conducive to digital preservation and transmission. Traditional image enhancement techniques, such as GAN networks that require paired images for training, are limited in their application to thangka datasets. To address this issue, we propose an image enhancement method called BM3D-UGanNet, which can be trained without paired data. this method combines the BM3D algorithm with the UGanNet network structure, effectively enhancing the visual quality of thangka images under low-light conditions while preserving their details and texture. this technology not only improves the viewing experience but also provides high-quality image resources for art research and education, holding significant academic and practical value. In addition, the low-light thangka dataset we have collected provides valuable resources for future research. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Gallium nitride

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CMME-YOLO: Composite Microscale Multi-stream Enhanced Model for PCB Defect Detection 19th

CMME-YOLO: Composite Microscale Multi-stream Enhanced Model ...

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19th chinese conference on image and graphics Technologies and applications, igta 2024

作者： Xie, Zhihua Zou, Xiaowei Zhou, Zhiwu Key Lab of Advanced Electronic Materials Jiangxi Science and Technology Normal University Nanchang China Nanchang Key Laboratory of Failure Perception & Green Energy Materials Intelligent Manufacturing Nanchang China

ISBN: (纸本)9789819799183

To address the challenges of excessive model parameters and low detection accuracy in print circuit board (PCB) defect detection, this paper proposes a novel PCB defect detection model based on the improved YOLOv8 (You Only Look Once v8), named CMME-Yolo. Specifically, the proposed model leverages the sliding window mechanism and the receptive field synthesis mechanism into the original YOLOv8 backbone. Particularly, this scheme proposes a fine aware aggregation paradigm to enhance the recognition of small-scale defects in PCB images and boost the detailed representation in YOLOv8. After effectively capturing fine-grained features from PCB images in the backbone, subtle features are strengthened through the SPD-Conv (Space-to-Depth) module in the neck, thus fulfilling fusion through multi-stream features. Finally, to achieve adaptive aggregation of fine-grained perceptual features, these processed features are fed into the bridge rectifier of the feature pyramid system—the Parallel Multi-stream Fine Aware Aggregation module (PMSFA). Extensive experiments are conducted on the PCB defect dataset released by Peking University to validate the effectiveness of the proposed model. the experimental results show that compared with the original YOLOv8, our approach achieved the improvement in mean Average Precision (mAP)@0.5 and mAP@0.5:0.95 by 4.9% and 12.1%, respectively. Meanwhile, the number of model parameters is reduced by approximately 14% compared with the original YOLOv8. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electric rectifiers

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image and graphics Technologies and applications 1

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丛书名： Communications in Computer and Information Science

1000年

作者： Wang Yongtian Wu Lifang

ISBN: (数字)9789819975495

ISBN: (纸本)9789819975488

this book constitutes the refereed proceedings of the 18th chinese conference on image and graphics Technologies and applications, igta 2023, held in Beijing, China, during August 17–19, 2023.;the 35 full papers included in this book were carefully reviewed and selected from 129 submissions. they were organized in topical sections as follows: image processing and enhancement techniques; machine vision and 3D reconstruction; image/video big data analysis and understanding; computer graphics; visualization and visual analysis; virtual reality and human-computer interaction; and applications of image and graphics.

关键词： Computer Imaging, Vision, Pattern Recognition and graphics Pattern Recognition Computer applications

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LR-SARNET: A Lightweight and Robust Network for Multi-scale and Multi-scene SAR Ship Detection 18th

LR-SARNET: A Lightweight and Robust Network for Multi-scale ...

引用

18th chinese conference on image and graphics technology and application conference, igta 2023

作者： Chang, Shibo Fu, Xiongjun Dong, Jian Chang, Hao School of Integrated Circuits and Electronics Beijing Institute of Technology Beijing100081 China

ISBN: (纸本)9789819975488

SAR Ship image has the characteristics of complex background, blurred target edge and scale difference, which makes the target detection difficult. this article builds a lightweight and robust network for multi-scale and multi-scene SAR ship detection (LR-SARNET). Firstly, with the goal to minimize the computational complexity of the model, a lightweight backbone feature extraction network (CGNet) is designed to generate sufficiently redundant feature maps with low computational cost. Secondly, a linear feature fusion module (ENECK) is designed to efficiently fuse deep local feature maps. Finally, the extremely efficient spatial pyramid (EESP) is integrated into the target detection head, which expands the receptive field of the network. the experiment on SSDD and HRSID dataset proves that our algorithm has strong robustness and excellent generalization performance. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Object detection

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A Perceptually Uniform Gloss Space for Translucent Materials 18th

A Perceptually Uniform Gloss Space for Translucent Materials

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18th chinese conference on image and graphics technology and application conference, igta 2023

作者： Huang, Shuo Li, Hongsong Zhang, Mingyuan School of Computer Science Beijing Institute of Technology Beijing100081 China

ISBN: (纸本)9789819975488

this study proposed a perceptual appearance model for translucent materials, addressing the issue of biased gloss perception by subsurface scattering. We established an empirical model relating the RMS slope first, which is a parameter of the BRDF model, to the measured perceived glossiness with a psychophysical experiment, and created a uniform perceived gloss space for opaque materials. For extending the space to cover the translucent materials, BSSRDF was incorporated to generate rendered objects with a lot of gloss and translucency. A second psychophysical experiment correlated perceived glossiness with both BRDF and BSSRDF, and built an empirical model that predicts the perceived glossiness of translucent materials with diverse surface and volumetric scattering properties. the developed model empowers designers to adjust translucency levels of translucent materials while maintaining perceived glossiness, which is proved by rendered images with various lighting conditions and object shapes. the proposed model provides a valuable tool for industrial design applications. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Product design

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Attention-Guided Neural Network for Face Mask Detection 18th

Attention-Guided Neural Network for Face Mask Detection

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18th chinese conference on image and graphics technology and application conference, igta 2023

作者： Zhang, Bowen Li, Shuyi Wang, Zhuming Wu, Lifang Faculty of Information Technology Beijing University of Technology Beijing100124 China

ISBN: (纸本)9789819975488

With the outbreak of COVID-19 and various influenza diseases, it is necessary to wear masks properly in crowded public places to prevent the spread of the virus. therefore, detecting mask-wearing efficiently and accurately is essential for people’s physical health and safety. In this paper, we present a novel one-stage mask detection method, named attention-guided neural network (AGNN) that can efficiently detect non-mask-wearing faces in public. Specifically, we started with YOLOv5 as a baseline and integrated the coordinate attention mechanism module into YOLOv5 to guide the holistic model for improving the ability of feature extraction. Furthermore, we explored utilizing the focal loss to solve the problem of class imbalance. the experiment is conducted on the face mask detection dataset of real-life scenes with twenty different categories. Experimental results demonstrate that the proposed AGNN method achieves higher precision and recall than the original YOLOv5 in multi-classification mask detection. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Viruses

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A Self-supervised Learning Reconstruction Algorithm with an Encoder-Decoder Architecture for Diffuse Optical Tomography 18th

A Self-supervised Learning Reconstruction Algorithm with an ...

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18th chinese conference on image and graphics technology and application conference, igta 2023

作者： Li, Yaxuan Wei, Chengpu Zhang, Wenqian Li, Zhe Sun, Zhonghua Jia, Kebin Feng, Jinchao Beijing Key Laboratory of Computational Intelligence and Intelligent System Faculty of Information Technology Beijing University of Technology Beijing100124 China Beijing Laboratory of Advanced Information Networks Beijing100124 China

ISBN: (纸本)9789819975488

Diffuse optical tomography (DOT) is an emerging non-invasive optical imaging technique, which has a promising application in breast cancer detection and diagnosis. However, the conventional image reconstruction algorithm in DOT is time-consuming and easy to error when recovering the distribution of optical parameters within the complete tissue. In this paper, we present an end-to-end reconstruction algorithm for DOT based on a deep convolutional encoder-decoder architecture, which consists of a data processing part and a convolutional encoder-decoder net. Its effectiveness was evaluated using simulation data. the results show that the overall quality of our method is significantly improved compared with the traditional algorithm based on the FEM method, the single inclusion deviation is reduced by 150% compared with the traditional algorithm, the standard deviation is reduced by 50%;multiple inclusions deviation is reduced by 100% and the standard deviation by 38.7%. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： image reconstruction

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