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1st International Scientific Conference on Advances in Science, Engineering and Digital Education, ASEDU 2020

作者： Mukhametzyanova, A.G. Bronskaya, V.V. Kharitonova, O.S. Department of Computer Graphics Engineering and Computer-Assisted Design Kazan National Research Technological University 68 Karl Marx Street Kazan420015 Russia Department of Chemical Process Engineering Kazan National Research Technological University 68 Karl Marx Street Kazan420015 Russia Department of Chemical Technology of Petroleum and Gas Processing Kazan National Research Technological University 68 Karl Marx Street Kazan420015 Russia

The importance of carrying competitions among students of general education institutions and the effectiveness of these activities as a means of developing the cognitive interest of students are shown. The article describes the experience of carrying competitions among schoolchildren and students of colleges, accumulated in the Kazan National Research Technological University. It is shown that these activities allow to reveal the creative potential of participants, and form the interest in engineering and design activities, which subsequently affects the choice of school leavers who are in demand in the modern labour market of specialties and professions. © 2020 Institute of Physics Publishing. All rights reserved.

关键词： Students

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HOnnotate: A Method for 3D Annotation of Hand and Object Poses

HOnnotate: A Method for 3D Annotation of Hand and Object Pos...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Shreyas Hampali Mahdi Rad Markus Oberweger Vincent Lepetit Institute for Computer Graphics and Vision Graz University of Technology Austria LIGM Ecole des Ponts Univ Gustave Eiffel CNRS Marne-la-Vallée France

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

We propose a method for annotating images of a hand manipulating an object with the 3D poses of both the hand and the object, together with a dataset created using this method. Our motivation is the current lack of annotated real images for this problem, as estimating the 3D poses is challenging, mostly because of the mutual occlusions between the hand and the object. To tackle this challenge, we capture sequences with one or several RGB-D cameras and jointly optimize the 3D hand and object poses over all the frames simultaneously. This method allows us to automatically annotate each frame with accurate estimates of the poses, despite large mutual occlusions. With this method, we created HO-3D, the first markerless dataset of color images with 3D annotations for both the hand and object. This dataset is currently made of 77,558 frames, 68 sequences, 10 persons, and 10 objects. Using our dataset, we develop a single RGB image-based method to predict the hand pose when interacting with objects under severe occlusions and show it generalizes to objects not seen in the dataset.

关键词： Three-dimensional displays Pose estimation Solid modeling Cameras Sensors Training Two dimensional displays

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Minimal Solutions for Relative Pose With a Single Affine Correspondence

Minimal Solutions for Relative Pose With a Single Affine Cor...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Banglei Guan Ji Zhao Zhang Li Fang Sun Friedrich Fraundorfer College of Aerospace Science and Engineering National University of Defense Technology China National University of Defense Technology Changsha China Institute for Computer Graphics and Vision Graz University of Technology Austria Remote Sensing Technology Institute German Aerospace Center Germany

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

In this paper we present four cases of minimal solutions for two-view relative pose estimation by exploiting the affine transformation between feature points and we demonstrate efficient solvers for these cases. It is shown, that under the planar motion assumption or with knowledge of a vertical direction, a single affine correspondence is sufficient to recover the relative camera pose. The four cases considered are two-view planar relative motion for calibrated cameras as a closed-form and a least-squares solution, a closed-form solution for unknown focal length and the case of a known vertical direction. These algorithms can be used efficiently for outlier detection within a RANSAC loop and for initial motion estimation. All the methods are evaluated on both synthetic data and real-world datasets from the KITTI benchmark. The experimental results demonstrate that our methods outperform comparable state-of-the-art methods in accuracy with the benefit of a reduced number of needed RANSAC iterations.

关键词： Cameras Pose estimation Transmission line matrix methods Robot vision systems Simultaneous localization and mapping Motion estimation

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The ZTF Source Classification Project: III. A Catalog of Variable Sources

arXiv

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arXiv 2023年

作者： Healy, Brian F. Coughlin, Michael W. Mahabal, Ashish A. Laz, Theophile Jegou du Drake, Andrew Graham, Matthew J. Hillenbrand, Lynne A. van Roestel, Jan Szkody, Paula Zielske, LeighAnna Guiga, Mohammed Hassan, Muhammad Yusuf Hughes, Jill L. Nir, Guy Parikh, Saagar Park, Sungmin Purohit, Palak Rebbapragada, Umaa Reed, Draco Warshofsky, Daniel Wold, Avery Bloom, Joshua S. Masci, Frank J. Riddle, Reed Smith, Roger School of Physics and Astronomy University of Minnesota MinneapolisMN55455 United States Division of Physics Mathematics and Astronomy California Institute of Technology PasadenaCA91125 United States Center for Data Driven Discovery California Institute of Technology PasadenaCA91125 United States Department of Astronomy California Institute of Technology PasadenaCA91125 United States Anton Pannekoek Institute for Astronomy University of Amsterdam Amsterdam1090 GE Netherlands Department of Astronomy University of Washington SeattleWA98195 United States Department of Physics Pacific Lutheran University TacomaWA98447 United States Department of Computer Science and Engineering University of Minnesota MinneapolisMN55455 United States Department of Electrical Engineering Indian Institute of Technology Gandhinagar Gujarat 382055 India Department of Astronomy University of California BerkeleyCA94720 United States Lawrence Berkeley National Laboratory 1 Cyclotron Road MS 50B-4206 BerkeleyCA94720 United States Department of Electrical and Computer Engineering Carnegie Mellon University PittsburghPA15213 United States Computer Vision Imaging and Graphics Lab Indian Institute of Technology Gujarat Gandhinagar382055 India School of Statistics University of Minnesota MinneapolisMN55455 United States Division of Engineering and Applied Science California Institute of Technology PasadenaCA91125 United States Jet Propulsion Laboratory California Institute of Technology PasadenaCA91125 United States IPAC California Institute of Technology 1200 E. California Blvd. PasadenaCA91125 United States Caltech Optical Observatories California Institute of Technology PasadenaCA91125 United States

The classification of variable objects provides insight into a wide variety of astrophysics ranging from stellar interiors to galactic nuclei. The Zwicky Transient Facility (ZTF) provides time-series observations that record the variability of more than a billion sources. The scale of these data necessitates automated approaches to make a thorough analysis. Building on previous work, this paper reports the results of the ZTF Source Classification Project (SCoPe), which trains neural network and XGBoost machine-learning (ML) algorithms to perform dichotomous classification of variable ZTF sources using a manually constructed training set containing 170,632 light curves. We find that several classifiers achieve high precision and recall scores, suggesting the reliability of their predictions for 209,991,147 light curves across 77 ZTF fields. We also identify the most important features for XGB classification and compare the performance of the two ML algorithms, finding a pattern of higher precision among XGB classifiers. The resulting classification catalog is available to the public, and the software developed for SCoPe is open-source and adaptable to future time-domain surveys. Copyright © 2023, The Authors. All rights reserved.

关键词： Classification (of information)

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Inferring the 3D Standing Spine Posture from 2D Radiographs

arXiv

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arXiv 2020年

作者： Bayat, Amirhossein Sekuboyina, Anjany Paetzold, Johannes C. Payer, Christian Stern, Darko Urschler, Martin Kirschke, Jan S. Menze, Bjoern H. Department of Informatics Technical University of Munich Germany Department of Neuroradiology Klinikum rechts der Isar Germany Institute of Computer Graphics and Vision Graz University of Technology Austria School of Computer Science University of Auckland New Zealand

The treatment of degenerative spinal disorders requires an understanding of the individual spinal anatomy and curvature in 3D. An upright spinal pose (i.e. standing) under natural weight bearing is crucial for such bio-mechanical analysis. 3D volumetric imaging modalities (e.g. CT and MRI) are performed in patients lying down. On the other hand, radiographs are captured in an upright pose, but result in 2D projections. This work aims to integrate the two realms, i.e. it combines the upright spinal curvature from radiographs with the 3D vertebral shape from CT imaging for synthesizing an upright 3D model of spine, loaded naturally. Specifically, we propose a novel neural network architecture working vertebra-wise, termed TransVert, which takes orthogonal 2D radiographs and infers the spine’s 3D posture. We validate our architecture on digitally reconstructed radiographs, achieving a 3D reconstruction Dice of 95.52%, indicating an almost perfect 2D-to-3D domain translation. Deploying our model on clinical radiographs, we successfully synthesise full-3D, upright, patient-specific spine models for the first time. Copyright © 2020, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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ALCN: Adaptive Local Contrast Normalization

arXiv

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arXiv 2020年

作者： Rad, Mahdi Roth, Peter M. Lepetit, Vincent Institute of Computer Graphics and Vision Graz University of Technology Graz Austria LIGM IMAGINE Ecole des Ponts Univ Gustave Eiffel CNRS Marne-la-Vallée France

To make Robotics and Augmented Reality applications robust to illumination changes, the current trend is to train a Deep Network with training images captured under many different lighting conditions. Unfortunately, creating such a training set is a very unwieldy and complex task. We therefore propose a novel illumination normalization method that can easily be used for different problems with challenging illumination conditions. Our preliminary experiments show that among current normalization methods, the Difference-of-Gaussians method remains a very good baseline, and we introduce a novel illumination normalization model that generalizes it. Our key insight is then that the normalization parameters should depend on the input image, and we aim to train a Convolutional Neural Network to predict these parameters from the input image. This, however, cannot be done in a supervised manner, as the optimal parameters are not known a priori. We thus designed a method to train this network jointly with another network that aims to recognize objects under different illuminations: The latter network performs well when the former network predicts good values for the normalization parameters. We show that our method significantly outperforms standard normalization methods and would also be appear to be universal since it does not have to be re-trained for each new application. Our method improves the robustness to light changes of state-of-the-art 3D object detection and face recognition methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Convolutional neural networks

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Force Field-Based Indirect Manipulation Of UAV Flight Trajectories*

Force Field-Based Indirect Manipulation Of UAV Flight Trajec...

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2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Werner Alexander Isop Friedrich Fraundorfer Department of Computer Science Institute of Computer Graphics and Vision Graz University of Technology Graz Austria

For a variety of applications remote navigation of an unmanned aerial vehicle (UAV) along a flight trajectory is an essential task. For instance, during search and rescue missions in outdoor scenes, an important goal is to ensure safe navigation. Assessed by the remote operator, this could mean avoiding collisions with obstacles, but moreover avoiding hazardous flight areas. State of the art approaches enable navigation along trajectories, but do not allow for indirect manipulation during motion. In addition, they suggest to use egocentric views which could limit understanding of the remote scene. With this work we introduce a novel indirect manipulation method, based on gravitational law, to recover safe navigation in the presence of hazardous flight areas. The indirect character of our method supports manipulation at far distances where common direct manipulation methods typically fail. We combine it with an immersive exocentric view to improve understanding of the scene. We designed three flavors of our method and compared them during a user study in a simulated scene. While with this method we present a first step towards a more extensive navigation interface, as future work we plan experiments in dynamic real-world scenes.

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Immersive Analytics of Anomalies in Multivariate Time Series Data with Proxy Interaction

Immersive Analytics of Anomalies in Multivariate Time Series...

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International Conference on Cyberworlds

作者： Simon Kloiber Josef Suschnigg Volker Settgast Christoph Schinko Martin Weinzerl Tobias Schreck Reinhold Preiner Institute of Computer Graphics and Knowledge Visualisation Graz University of Technology Austria Pro2Future GmbH Austria Fraunhofer Austria Research GmbH Austria AVL List GmbH Austria

ISBN: (数字)9781728164977

ISBN: (纸本)9781728164984

In industry and science, sensor data play a vital role in research, optimisation, monitoring, testing and many other use cases. When performing tests with repeated cycles of similar behaviour, e.g., durability tests, it is often important to find anomalous sensor behaviour that deviates from regular patterns in the data. We here explore the design space of VRbased immersive analytics for time series data, for use e.g., in engineering contexts where an underlying application is also given in VR. The use of 3D visualisation for time series exploration is a much-discussed topic and careful consideration for its use must be taken. With the rise of immersive environments, we re-visit the classic problem of 3D time series visualisation and introduce an immersive walk-up usable interaction proxy that supports efficient navigation of otherwise possibly occluded time series views. The proxy indicates anomalies in the data for easy access and provides efficient zooming and filtering controls, among other effective interaction possibilities. This approach is combined with suitable data analysis techniques, providing an environment for effective and efficient immersive anomaly detection and comparative data analysis that we call WaveCharts. We demonstrate the applicability of our approach by two real-world use cases, and we discuss the necessary tools it provides to aid the analysis process of large sensor data.

关键词： Three-dimensional displays Data analysis Time series analysis Data visualization Tools Space exploration Testing

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AB1556 MACHINE LEARNING APPROACH FOR CLASSIFICATION OF ARTHRITIS ACTIVITY STATE, USING DATA FROM A SINGLE ACCELEROMETER

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Annals of the Rheumatic Diseases 2023年 82卷 2012-2012页

作者： P. Mielnik A. Myhre Hjelle A. Traseth K. Tokarz B. Pollen M. Fojcik Helse Forde Section for Rheumatology； Department for Neurology Rheumatology and Physical Medicine Forde Norway Institute of Informatics Silesian University of Technology Department of Graphics Computer Vision and Digital Systems Gliwice Poland Western Norway University of Applied Sciences Faculty of Engineering and Science Forde Norway

Background Machine learning (ML) potential is not fully exploited in diagnostics and follow up of autoimmune inflammatory rheumatic disease (AIIRD). It is despite the broader use of ML in, e.g. imaging diagnostics. The specific tools for AIIRD are lacking. Objectives This is an interim analysis of data from the first checkpoint of the proof-of-concept study on using accelerometer (ACC) data in follow up of patients with AIIRD. The main goal of the study was to investigate the value of single ACC data in the classification of arthritis activity status. Methods Subjects with AIIRD are enrolled in the study when they start of new treatment due to the disease activity. Several comorbidities, such as severe neurological and cardiological disorders, as wells as impaired mobility, are part of the protocol exclusion criteria. Volunteers without AIIRD who fulfil other inclusion/exclusion criteria are included as controls. The study was approved by local competent authorities and informed consent was given by all participants. We used data collected up till the first study checkpoint. This analysis covers nine patients with AIIRD (5 rheumatoid and four psoriasis arthritis) and 13 controls. Five patients had 3 study visits, 1 had 2 visits and 3 only one. Controls have only one visit per protocol. We analysed ACC data from 3 minutes of clapping using a home-brewed device based on Arduino nano 33 BLE with 6-axis MTU by Nordic Semiconductor. Data was divided into 6-second chunks that were found optimal in our prior study. We conducted binary classification between any/ no arthritis in any of the upper extremities. We used accuracy and area under curve (AUC) as an efficacy function derived from the receiver operating characteristic curve (ROC). We extracted 54 features from 3 ACC axis signal. The features encompassed, among others, Fourier's components, autoregression coefficients, median absolute deviation (MAD), variance, Fourier's entropy, etc. We built linear discriminant anal

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Rotational alignment of imu-camera systems with 1-point ransac 2nd

Rotational alignment of imu-camera systems with 1-point rans...

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2nd Chinese Conference on Pattern Recognition and computer Vision, PRCV 2019

作者： Guan, Banglei Su, Ang Li, Zhang Fraundorfer, Friedrich College of Aerospace Science and Engineering National University of Defense Technology Changsha China Hunan Provincial Key Laboratory of Image Measurement and Vision Navigation Changsha China Institute for Computer Graphics and Vision Graz University of Technology Graz Austria

ISBN: (纸本)9783030317256

In this paper we present a minimal solution for the rotational alignment of IMU-camera systems based on a homography formulation. The image correspondences between two views are related by homography when the motion of the camera can be effectively approximated as a pure rotation. By exploiting the rotational angles of the features obtained by e.g. the SIFT detector, we compute the rotational alignment of IMU-camera systems with only 1 feature correspondence. The novel minimal case solution allows us to cope with feature mismatches efficiently and robustly within a random sample consensus RANSAC scheme. Our method is evaluated on both synthetic and real scene data, demonstrating that our method is suited for the rotational alignment of IMU-camera systems. © Springer Nature Switzerland AG 2019.

关键词： Cameras

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