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检索条件"任意字段=Proceedings of the Workshop on Physics-Based Modeling in Computer Vision"

共 521 条记录，以下是1-10 订阅

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Predicting 3D Projectile Motion in Table Tennis Using computer vision and physics-Informed Neural Network 21

Predicting 3D Projectile Motion in Table Tennis Using Comput...

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21st International Conference on Content based Multimedia Indexing

作者： Chiha, Zaineb Peteri, Renaud Mascarilla, Laurent La Rochelle Univ MIA La Rochelle France

ISBN: (纸本)9798350378443;9798350378450

vision-based methods for gesture analysis in sports can provide valuable insights to players and coaches, without any disturbing body-worn sensors. In table tennis, accurately predicting the trajectories of the ball and its 3D kinematics is crucial for assessing the effectiveness of a stroke. While several computer vision methods have investigated this area, they often focus on trajectory prediction solely, without considering physical parameters. Additionally, these studies are typically limited to controlled laboratory settings, which can be invasive and require extensive data collection. In this paper, we present a novel vision-based approach to 3D ball trajectory analysis. Our proposed method relies on physics-Informed Learning to predict the 3D trajectory of a table tennis ball and to infer its 3D kinematics parameters using a small amount of data acquired from video sequences. To the best of our knowledge, this is the first attempt to integrate data-driven approaches with physical constraints in order to predict the 3D trajectory and kinematics parameters of a table tennis ball.

关键词： computer vision PINNs 3D kinematics Projectile Motion Table Tennis

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NRTK: an open source natural robustness toolkit for the evaluation of computer vision models

NRTK: an open source natural robustness toolkit for the eval...

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Conference on Assurance and Security for AI-Enabled Systems

作者： Hu, Brian RichardWebster, Brandon Tunison, Paul Veenhuis, Emily Ravichandran, Bharadwaj Lynch, Alexander Crowell, Stephen Genova, Alessandro Bolea, Vicente Jourdain, Sebastien Whitesell, Austin Kitware Inc 1712 Route 9 300 Clifton Pk NY 12065 USA Mitre Corp 7525 Colshire Dr Mclean VA USA

ISBN: (纸本)9781510674271;9781510674264

Recent progress in computer vision has been fueled by advances in artificial intelligence (AI) using techniques such as deep learning. However, computer vision models are often sensitive to distribution shifts, such as those naturally occurring in real operational data, which can degrade performance and cause unforeseen failure modes. As such, it is critical to perform comprehensive test and evaluation of these models across a wide range of natural effects in order to assure their downstream use and deployment. However, it is often difficult, impractical or simply impossible to collect and annotate datasets incorporating the expected range of conditions in operational domains. Here, we introduce the open source natural robustness toolkit (NRTK), a platform for evaluating the natural robustness of computer vision models through realistic augmentations of datasets. The NRTK package provides a set of validated scene- and sensor-specific perturbations using physics-based modeling of the entire image formation process, covering pre-sensor, at-sensor, and post-sensor perturbations. We demonstrate its modular and flexible nature through the example use cases of evaluating object detection models on satellite and unmanned aerial vehicle (UAV) imagery, where critical sensor parameters such as focal length and aperture diameter can be varied to assess model performance. We also show how the NRTK package can be paired with visualization tools to support the interactive exploration of datasets and their perturbations via NRTK. Our results suggest that natural robustness is a critical dimension for evaluation towards the verification and validation of computer vision models, ensuring their trusted use and deployment. Our code is publicly available at: https://***/Kitware/nrtk.

关键词： computer vision AI assurance test and evaluation robustness sensors modeling and simulation aerial imagery

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PGNN-based Approach for Robust 3D Light Direction Estimation in Outdoor Images 21

PGNN-based Approach for Robust 3D Light Direction Estimation...

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21st International Conference on Content based Multimedia Indexing

作者： Zanardelli, Marcello Gohari, Mahyar Leonardi, Riccardo Benini, Sergio Adami, Nicola Univ Brescia Dept Informat Engn Brescia Italy

ISBN: (纸本)9798350378443;9798350378450

Estimating the 3D light direction from 2D outdoor images is a crucial task in computer vision, especially useful in the context of image forgery detection. In this paper, we propose a novel approach that leverages a physics-guided neural network (PGNN) to achieve accurate global 3D light direction estimation. The proposed architecture incorporates an illumination model that enables the network to indirectly learn geometric information and improve the accuracy of the estimated light direction. To evaluate the performance of our proposed approach, we train and test our models on two datasets that were curated for the global light direction estimation. The proposed PGNN method demonstrates superior performance compared to existing state-of-the-art approaches, where direct comparisons were feasible. To further investigate the benefits provided by embedding the physical model in our approach, we conducted an extensive ablation study, demonstrating that the use of the illumination model significantly enhances the accuracy of the light direction estimation compared to a purely data-driven approach. The proposed PGNN is available as open-source software, providing an accessible and useful tool for researchers in computer vision and graphics.

关键词： physics-guided neural networks 3D light estimation scene understanding image forgery

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32nd International Conference in Central Europe on computer Graphics, Visualization and computer vision, WSCG 2024 - proceedings

32nd International Conference in Central Europe on Computer ...

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32nd International Conference in Central Europe on computer Graphics, Visualization and computer vision, WSCG 2024

The proceedings contain 46 papers. The topics discussed include: a synergy of computer graphics and generative AI: advancements and challenges;the impact of the number of k-means clusters on 3D point cloud registration;automated bioacoustic monitoring for South African bird species on unlabeled data;beyond the benchmark: detecting diverse anomalies in videos;attention-aware DAE for automated solar coronal loop segmentation;empathy training using virtual environments;texture-based global illumination for physics-based light propagation in interactive web applications;improving image reconstruction using incremental PCA-embedded convolutional variational auto-encoder;reevaluation in rule-based graph transformation modeling systems;towards multi-species animal re-identification;and texture reading optimization for 2 dimensional filter based graphics algorithms.

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Application of vision transformers and 3D convolutional neural networks for sign language cluster recognition 6

Application of vision transformers and 3D convolutional neur...

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6th International workshop on computer modeling and Intelligent Systems, CMIS 2023

作者： Smirnov, Serhii Kuznietsova, Nataliia National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute" ave. Peremohy 37 Kyiv03056 Ukraine

In this study the overview of the methods for sign language recognition was done and the existing datasets in this area were analyzed. It was shown how based on the real data to develop and test different approaches. The models based on the vision Transformer (ViViT) and 3D Convolutions CNN (3dCNN) using different batch sizes were built and compared. It was also shown how to learn models on different data sizes and to search the compromise between accuracy, speed and overfitting of the models. Our research provides valuable insights into the strengths and limitations of different models of the task, not solved tasks and offer a direction and possible improvements of existed methods in this area by using vision transformers. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： computer vision

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Wavelet transform based optimization method for Three-Dimensional computer vision 12

Wavelet transform based optimization method for Three-Dimens...

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12th International Scientific and Practical Conference "Information Control Systems and Technologies", ICST 2024

作者： Antoshchuk, Svetlana Shcherbakova, Galina Kondratyev, Sergey Koshutina, Daria Usov, Oleksandr Odessa Polytechnic National University Shevchenko Ave. 1 Odessa65044 Ukraine

This work aims to develop approximate depth estimation methods for three-dimensional computer vision in unmanned vehicles (UVs) based on a developed wavelet transformation-based optimization method. This method can assess informative features for matching and/or optimizing costs in image analysis, refining discrepancies, and more. Possible solutions are demonstrated for obtaining an approximate depth map by simplifying the calculation of disparity values, traditionally used for forming a depth map in intensity space and using edge description with adjustable detail based on wavelet transformation. The advantage of the developed optimization method over existing algorithms in the wavelet space is the increased speed due to the rational selection of the Haar wavelet support length in the extremum search area. modeling confirmed the effectiveness of the proposed approach for constructing depth maps and allowed for recommending the proposed method for unmanned vehicles operating under limited computational and energy resources. © 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Contourlet transform

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Enhancing recyclable waste classification based on deep residual learning 4

Enhancing recyclable waste classification based on deep resi...

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4th International Conference on computer vision and Pattern Analysis, ICCPA 2024

作者： Jiang, Xingsheng School of Physics and Electronic Engineering Northeast Petroleum University Heilongjiang Province Daqing163318 China

ISBN: (纸本)9781510682528

This study aims to leverage advanced deep learning technologies to facilitate high-accuracy waste classification on mobile devices. We developed a deep neural network model based on ResNet-34, specifically designed for the automatic identification and classification of recyclable waste images. Utilizing a residual learning framework, this model enhances the representational capacity of feature maps, effectively captures deeper features, and maintains information flow, thus preventing the common issue of feature degradation during deep network training. Testing on the TrashNet dataset demonstrated that this model surpasses other common convolutional neural network architectures in multiple performance metrics, including accuracy, precision, recall, and F1 score, achieving a classification accuracy of 86.25% and confirming its efficacy in handling complex waste classification tasks. © 2024 SPIE.

关键词： Data modeling Performance modeling Education and training Instrument modeling Image classification Feature extraction Mobile devices Deep learning Image processing Convolution

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Research on motion behavior control of the carp robot based on computer vision

Research on motion behavior control of the carp robot based ...

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2024 International workshop on Automation, Control, and Communication Engineering, IWACCE 2024

作者： Zhao, Yang Peng, Yong Li, Xue Wang, Rongyan Yan, Yanhong Sun, Shuhao Dong, Xueying Jiang, Kedong Zhang, Xiaowei School of Electrical Engineering Yanshan University Qinhuangdao Hebei066004 China School of Mechanical Engineering Yanshan University Qinhuangdao Hebei066004 China Shijiazhuang University Shijiazhuang Hebei050035 China

ISBN: (数字)9781510685420

ISBN: (纸本)9781510685413

Bio-robot is a kind of creature controlled by human beings by applying intervention signals through control technology to regulate biological behavior. The animal can be controlled artificially and become a kind of animal robot by stimulating the brain motor area of the animal. To realize the precise control of the carp robot, it is necessary to monitor its motion state and quantify its motion behavior. Therefore, the article proposes a method of applying computer vision technology to detect the control effect of the carp robot. In this study, the method of combining inter-frame difference with Gaussian mixture background modeling was used to extract the target, the mass cancroid tracking algorithm was used to track the target, and the parallax method was used to obtain the three-dimensional motion trajectory and various motion parameters. The carp robots were divided into the Experimental group and the Control group, with 30 fish in each group. In the Experimental group, the computer vision technology was used for real-time detection, and electrical stimulation parameters was adjusted according to the measured data to control the movement of the carp robot. In the Control group, the underwater control experiment was carried out without computer vision technology. The results showed that the success rate of control in the Experimental group was significantly higher than that of the Control group (P © 2024 SPIE. All rights reserved.

关键词： Machine vision

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DigiTransfEd 2024 - proceedings of the 3rd workshop on Digital Transformation of Education, co-located with 19th International Conference on ICT in Education, Research and Industrial Applications, ICTERI 2024

DigiTransfEd 2024 - Proceedings of the 3rd Workshop on Digit...

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3rd workshop on Digital Transformation of Education, DigiTransfEd 2024

The proceedings contain 12 papers. The topics discussed include: from digital strategies to innovative integration: advancing educational practices through technology at DigiTransfEd 2024;developing and validating an integrated three-tier multiple choice test for smartphone-based experiments in physics;gamification method using Minecraft for training future teachers of computer science;professional development of teachers in conditions of digital transformation of postgraduate pedagogical education;development of digital competence of the military leadership officers in the system of advanced training: a pedagogical experiment;visual modeling technologies in the mathematical training of engineers in modern conditions of digital transformation of approaches to creating visual content;school digitalization indicators in educational equity analysis;and semantic analysis of learning objects: thesaurus approach for digital transformation of educational resources.

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Mitigating Motion Blur for Robust 3D Baseball Player Pose modeling for Pitch Analysis 6

Mitigating Motion Blur for Robust 3D Baseball Player Pose Mo...

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6th ACM International workshop on Multimedia Content Analysis in Sports (MMSports)

作者： Bright, Jerrin Chen, Yuhao Zelek, John Univ Waterloo Waterloo ON Canada

ISBN: (纸本)9798400702693

Using videos to analyze pitchers in baseball can play a vital role in strategizing and injury prevention. computer vision-based pose analysis offers a time-efficient and cost-effective approach. However, the use of accessible broadcast videos, with a 30fps framerate, often results in partial body motion blur during fast actions, limiting the performance of existing pose keypoint estimation models. Previous works have primarily relied on fixed backgrounds, assuming minimal motion differences between frames, or utilized multiview data to address this problem. To this end, we propose a synthetic data augmentation pipeline to enhance the model's capability to deal with the pitcher's blurry actions. In addition, we leverage in-the-wild videos to make our model robust under different real-world conditions and camera positions. By carefully optimizing the augmentation parameters, we observed a notable reduction in the loss by 54.2% and 36.2% on the test dataset for 2D and 3D pose estimation respectively. By applying our approach to existing state-of-the-art pose estimators, we demonstrate an average improvement of 29.2%. The findings highlight the effectiveness of our method in mitigating the challenges posed by motion blur, thereby enhancing the overall quality of pose estimation.

关键词： Sabermetrics Baseball Analysis Pose Estimation 3D Body modeling Motion Blur SMPL vision Transformers Optimization DTW

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