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Force Field-Based Indirect Manipulation Of UAV Flight Trajectories*

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

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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Inferring the 3D Standing Spine Posture from 2D Radiographs

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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

Lossless compression of multi-view cultural heritage image data

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Lossless compression of multi-view cultural heritage image d...

2019 Eurographics Workshop on graphics and Cultural Heritage, GCH 2019

作者： Buelow, Max Von Guthe, Stefan Ritz, Martin Santos, Pedro Fellner, Dieter W. TU Darmstadt Germany Fraunhofer IGD Germany Graz University of Technology Institute of Computer Graphics and Knowledge Visualization Austria

ISBN: (纸本)9783038680826

Photometric multi-view 3D geometry reconstruction and material capture are important techniques for cultural heritage digitalization. Capturing images of artifacts with high resolution and high dynamic range and the possibility to store them losslessly enables future proof application of this data. As the images tend to consume immense amounts of storage, compression is essential for long time archiving. In this paper, we present a lossless image compression approach for multi-view and material reconstruction datasets with a strong focus on data created from cultural heritage digitalization. Our approach achieves compression rates of 2:1 compared against an uncompressed representation and 1.24:1 when compared against Gzip. © 2019 Eurographics Association.

关键词： Image reconstruction

Semi-supervised Detector Training with Prototypes for Vehicle Detection

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Semi-supervised Detector Training with Prototypes for Vehicl...

International Conference on Intelligent Transportation

作者： Georg Waltner Michael Opitz Georg Krispel Horst Possegger Horst Bischof Institute of Computer Graphics and Vision (ICG) Faculty of Computer Science Graz University of Technology Graz Austria

Adapting detectors to new datasets is needed in scenarios where a user has a specific dataset that contains novel classes or is recorded in a setting where a pretrained detector fails. While detectors based on Convolutional Neural Networks (CNNs) are state-of-the-art and nowadays publicly available, they suffer from bad generalization capabilities when applied on datasets that notably differ from the one they were trained on. Finetuning the detector is only possible if the dataset is large enough to not destroy the underlying feature representation. We propose a method where only a few prototypes are labeled for training in a semi-supervised manner. In particular, we separate the detection from the classification step to avoid impairing the bounding box proposal generation. Our trained prototype classification network provides labels to automatically source a large dataset containing 20 to 30 times more samples without further supervision, which we then use to train a more powerful network. We evaluate our method on a private vehicle dataset with six classes and show that evaluating on a previously unseen recording site we can gain an accuracy increase of 9% at same precision and recall levels. We further show that finetuning with as few as 25 labeled samples per class doubles accuracy compared to directly using pretrained features for nearest neighbor classification.

关键词： Detectors Proposals Prototypes Training Object detection Feature extraction Task analysis

A client/server based online environment for manual segmentation of medical images

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arXiv 2019年

作者： Wild, Daniel Weber, Maximilian Egger, Jan Institute of Computer Graphics and Vision Graz University of Technology Graz Austria Computer Algorithms for Medicine Lab Graz Austria

Segmentation is a key step in analyzing and processing medical images. Due to the low fault tolerance in medical imaging, manual segmentation remains the de facto standard in this domain. Besides, efforts to automate the segmentation process often rely on large amounts of manually labeled data. While existing software supporting manual segmentation is rich in features and delivers accurate results, the necessary time to set it up and get comfortable using it can pose a hurdle for the collection of large datasets. This work introduces a client/server based online environment, referred to as Studierfenster, that can be used to perform manual segmentations directly in a web browser. The aim of providing this functionality in the form of a web application is to ease the collection of ground truth segmentation datasets. Providing a tool that is quickly accessible and usable on a broad range of devices, offers the potential to accelerate this process. The manual segmentation workflow of Studierfenster consists of dragging and dropping the input file into the browser window and sliceby- slice outlining the object under consideration. The final segmentation can then be exported as a file storing its contours and as a binary segmentation mask. In order to evaluate the usability of Studierfenster, a user study was performed. The user study resulted in a mean of 6.3 out of 7.0 possible points given by users, when asked about their overall impression of the tool. The evaluation also provides insights into the results achievable with the tool in practice, by presenting two ground truth segmentations performed by physicians. Copyright © 2019, The Authors. All rights reserved.

关键词： Large dataset

Apple vision Pro for Healthcare: "The Ultimate Display"? Entering the Wonderland of Precision Medicine

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arXiv 2023年

作者： Egger, Jan Gsaxner, Christina Chen, Xiaojun Bian, Jiang Kleesiek, Jens Puladi, Behrus Girardetstraße 2 Essen45131 Germany Hufelandstraße 55 Essen45147 Germany Hufelandstraße 55 Essen45147 Germany Department of Oral and Maxillofacial Surgery University Hospital RWTH Aachen Pauwelsstraße 30 Aachen52074 Germany Institute of Computer Graphics and Vision Graz University of Technology Inffeldgasse 16/II Graz8010 Austria Institute of Biomedical Manufacturing and Life Quality Engineering State Key Laboratory of Mechanical System and Vibration School of Mechanical Engineering Shanghai Jiao Tong University Dongchuan Road 800 Minhang District Shanghai200240 China Institute of Medical Robotics Shanghai Jiao Tong University Dongchuan Road 800 Minhang District Shanghai200240 China Health Outcomes & Biomedical Informatics College of Medicine University of Florida GainesvilleFL32610 United States Partner Site Essen Hufelandstraße 55 Essen45147 Germany Department of Physics TU Dortmund University August-Schmidt-Str. 4 Dortmund44227 Germany Institute of Medical Informatics University Hospital RWTH Aachen Pauwelsstraße 30 Aachen52074 Germany

At the Worldwide Developers Conference (WWDC) in June 2023, Apple introduced the vision Pro. The vision Pro is a Mixed Reality (MR) headset, more specifically it is a Virtual Reality (VR) device with an additional Video See-Through (VST) capability. The VST capability turns the vision Pro also into an Augmented Reality (AR) device. The AR feature is enabled by streaming the real world via cameras to the (VR) screens in front of the user’s eyes. This is of course not unique and similar to other devices, like the Varjo XR-3. Nevertheless, the vision Pro has some interesting features, like an inside-out screen that can show the headset wearers’ eyes to "outsiders" or a button on the top, called "Digital Crown", that allows you to seamlessly blend digital content with your physical space by turning it. In addition, it is untethered, except for the cable to the battery, which makes the headset more agile, compared to the Varjo XR-3. This could actually come closer to the "Ultimate Display", which Ivan Sutherland had already sketched in 1965. Not available to the public yet, like the Ultimate Display, we want to take a look into the crystal ball in this perspective to see if it can overcome some clinical challenges that - especially - AR still faces in the medical domain, but also go beyond and discuss if the vision Pro could support clinicians in essential tasks to spend more time with their patients. © 2023, CC BY.

关键词： Augmented reality

Image morphing in deep feature spaces: Theory and applications

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arXiv 2019年

作者： Effland, Alexander Kobler, Erich Pock, Thomas Rajković, Marko Rumpf, Martin Institute of Computer Graphics and Vision Graz University of Technology Institute for Numerical Simulation University of Bonn

This paper combines image metamorphosis with deep features. To this end, images are considered as maps into a high-dimensional feature space and a structure-sensitive, anisotropic flow regularization is incorporated in the metamorphosis model proposed by Miller, Trouvé, Younes and coworkers [MY01, TY05b]. For this model a variational time discretization of the Riemannian path energy is presented and the existence of discrete geodesic paths minimizing this energy is demonstrated. Furthermore, convergence of discrete geodesic paths to geodesic paths in the time continuous model is investigated. The spatial discretization is based on a finite difference approximation in image space and a stable spline approximation in deformation space, the fully discrete model is optimized using the iPALM algorithm. Numerical experiments indicate that the incorporation of semantic deep features is superior to intensity-based *** Codes 65D18, 37L65, 49M25, 53C22, 65L20 Copyright © 2019, The Authors. All rights reserved.

关键词： Finite difference method

On the estimation of the Wasserstein distance in generative models

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arXiv 2019年

作者： Pinetz, Thomas Soukup, Daniel Pock, Thomas Center for Vision Automation and Control Austrian Institute of Technology Institute of Computer Graphics and Vision Graz University of Technology Austria

Generative Adversarial Networks (GANs) have been used to model the underlying probability distribution of sample based datasets. GANs are notoriuos for training diffculties and their dependence on arbitrary hyperparameters. One recent improvement in GAN literature is to use the Wasserstein distance as loss function leading to Wasserstein Generative Adversarial Networks (WGANs). Using this as a basis, we show various ways in which the Wasserstein distance is estimated for the task of generative modelling. Additionally, the secrets in training such models are shown and summarized at the end of this work. Where applicable, we extend current works to different algorithms, different cost functions, and different regularization schemes to improve generative models. Copyright © 2019, The Authors. All rights reserved.

关键词： Generative adversarial networks

Patch augmentation: Towards efficient decision boundaries for neural networks

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arXiv 2019年

作者： Bloice, Marcus D. Roth, Peter M. Holzinger, Andreas Institute for Medical Informatics Statistics and Documentation Medical University Graz Institute of Computer Graphics and Vision Graz University of Technology

In this paper we propose a new augmentation technique, called patch augmentation, that, in our experiments, improves model accuracy and makes networks more robust to adversarial attacks. In brief, this data-independent approach creates new image data based on image/label pairs, where a patch from one of the two images in the pair is superimposed on to the other image, creating a new augmented sample. The new image's label is a linear combination of the image pair's corresponding labels. Initial experiments show a several percentage point increase in accuracy on CIFAR-10, from a baseline of approximately 81% to 89%. CIFAR-100 sees larger improvements still, from a baseline of 52% to 68% accuracy. Networks trained using patch augmentation are also more robust to adversarial attacks, which we demonstrate using the Fast Gradient Sign Method. Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning