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2019 Conference on Vision, Modeling and visualization, VMV 2019

作者： Kern, Michael Westermann, Rüdiger Technische Universität München Chair of Computer Graphics and Visualization Germany

ISBN: (纸本)9783038680987

The extraction of a jet-stream core line in a wind field results in many disconnected line segments of arbitrary topology. In an ensemble of wind fields, these structures show high variation, coincide only partly, and almost nowhere agree in all ensemble members. In this paper, we shed light on the use of clustering for visualizing an ensemble of jet-stream core lines. Since classical approaches for clustering 3D line sets fail due to the mentioned properties, we analyze different strategies and compare them to each other: We cluster the 3D scalar fields from which jet-stream core lines are extracted. We cluster on a closest-point representation of each set of core lines. These representations are derived from the extracted line geometry and can be used independently of the line orientation and topology. We cluster on the 3D line set using the Hausdorff distance as similarity metric. In the resulting clusters, we visualize core lines from the most representative ensemble member. We further compute ridges in a single 3D visitation map that is build from the ensemble of core lines, and we extract the most central core line from the ensemble closest-point representation. These "averages" are compared to the clustering results, and they are put into relation to ground truth jet-stream core lines at the predicted lead time. © 2019 The Author(s) Eurographics Proceedings © 2019 The Eurographics Association.

关键词： Topology

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Teaching Distributed and Heterogeneous Robotic Cells 19

Teaching Distributed and Heterogeneous Robotic Cells

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19th IEEE Annual Consumer Communications and Networking Conference, CCNC 2022

作者： Mey, Johannes Ebert, Sebastian Lin, Tianfang Nguyen, Giang T. Gumhold, Stefan Asmann, Uwe Technische Universität Chair of Software Engineering Dresden Germany Technische Universität Chair of Computer Graphics and Visualization Dresden Germany Technische Universität Chair of Haptic Communication Systems Dresden Germany Dresden Germany

ISBN: (纸本)9781665431620

Teaching heterogeneous robotic cells is difficult and time-consuming. We present a context-aware robot teaching solution based on an open-source framework that decouples the task instruction from the task execution to simplify the teaching workflow significantly. To demonstrate the advantages of the solution, a human operator uses a VR headset and controllers to teach a virtual robot to perform a task, e.g., pick-and-place in a virtual world. The task is automatically adapted to different settings of operation cells and executed by physical robots located at different geographical locations. © 2022 IEEE.

关键词： Virtual reality

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Understanding multi-modal brain network data: An immersive 3D visualization approach

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computers and graphics (Pergamon) 2022年 106卷 88-97页

作者： Pester, Britta Russig, Benjamin Winke, Oliver Ligges, Carolin Dachselt, Raimund Gumhold, Stefan Chair for Computer Graphics and Visualization TU Dresden Germany Jena University Hospital Department of Child and Adolescent Psychiatry Psychosomatic Medicine and Psychotherapy Friedrich Schiller University Jena Germany Interactive Media Lab Dresden TU Dresden Germany TU Dresden Germany Germany

Understanding the human brain requires the incorporation of functional interaction patterns that depend on a variety of features like experimental setup, strength of directed connectedness or variability between several individuals or groups. In addition to these external factors, there are internal properties of the brain network as for example temporal propagation of connections, or connectivity patterns that only occur in a distinct frequency range of the signal. The visualization of detected networks covering all necessary information poses a substantial problem which is mainly due to the high number of features that have to be integrated within the same view in a natural spatial context. To address this problem, we propose a new tool that transfers the network into an anatomically arranged origin–destination view in a virtual visual analysis lab. This offers the user an opportunity to assess the temporal evolution of connectivity patterns and provides an intuitive and motivating way of exploring the corresponding features via navigation and interaction in virtual reality (VR). The approach was evaluated in a user study including participants with neuroscientific background as well as people working in the field of computer science. As a first proof of concept trial we used functional brain networks derived from time series of electroencephalography recordings evoked by visual stimuli. All participants gave a positive general feedback, notably they saw a benefit in using the VR view instead of the compared 2D desktop variant. This suggests that our application successfully fills a gap in the visualization of high-dimensional brain networks and that it is worthwhile to further follow and enhance the proposed representation method. © 2022 Elsevier Ltd

关键词： Electroencephalography

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Inverting the Feature visualization Process for Feedforward Neural Networks

arXiv

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arXiv 2020年

作者： Reinbold, Christian Westermann, Rüdiger Chair of Computer Graphics and Visualization Technical University of Munich Bavaria Germany

This work sheds light on the invertibility of feature visualization in neural networks. Since the input that is generated by feature visualization using activation maximization does, in general, not yield the feature objective it was optimized for, we investigate optimizing for the feature objective that yields this input. Given the objective function used in activation maximization that measures how closely a given input resembles the feature objective, we exploit that the gradient of this function w.r.t. inputs is—up to a scaling factor—linear in the objective. This observation is used to find the optimal feature objective via computing a closed form solution that minimizes the gradient. By means of Inverse Feature visualization, we intend to provide an alternative view on a networks sensitivity to certain inputs that considers feature objectives rather than activations. Copyright © 2020, The Authors. All rights reserved.

关键词： visualization

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An Immersive Method for Extracting Structural Information from Unorganized Point Clouds

An Immersive Method for Extracting Structural Information fr...

引用

Nicograph International (NicoInt)

作者： Zhongyuan Yu Tianfang Lin Stefan Gumhold Matthew McGinity Immersive Experience Lab TU Dresden Chair of Computer Graphics and Visualization TU Dresden

ISBN: (数字)9798350386882

ISBN: (纸本)9798350386899

Point clouds find extensive applications across diverse domains such as construction, architecture, and archaeology due to their capacity to encapsulate rich datasets. Given the density and detail of these datasets, extracting accurate structural information (geometrical and connectivity information) demands sophisticated algorithms and tools. The recent advancements in virtual reality (VR) technology and rendering techniques have significantly elevated the ability to visualize these point cloud datasets, affording users an immersive experience within the data. This immersive capability holds the potential to streamline the process of extracting valuable information from these datasets. In this work, we explore the feasibility of extracting and visualizing structural information from point clouds with immersive interfaces. We propose strategies to enhance human perception of the underlying structure and interactive methods to extract face, edge, vertex, and connectivity data from the raw, unorganized point clouds. Our method is evaluated based on public datasets. We believe our work represents a promising attempt to integrate human insights into the data processing pipeline.

关键词： Point cloud compression Geometry Pipelines Data visualization Prototypes Immersive experience Rendering (computer graphics)

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Deep Light Direction Reconstruction from single RGB images 29

Deep Light Direction Reconstruction from single RGB images

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29th International Conference in Central Europe on computer graphics, visualization and computer Vision, WSCG 2021

作者： Miller, Markus Nischwitz, Alfred Westermann, Rüdiger Dept. of Computer Science and Mathematics University of Applied Sciences Munich Lothstr. 64 Bavaria MunichD-80335 Germany Chair of Computer Graphics and Visualization Technical University Munich Boltzmannstr. 3/II Bavaria GarchingD-85748 Germany

ISBN: (纸本)9788086943343

In augmented reality applications, consistent illumination between virtual and real objects is important for creating an immersive user experience. Consistent illumination can be achieved by appropriate parameterisation of the virtual illumination model, that is consistent with real-world lighting conditions. In this study, we developed a method to reconstruct the general light direction from red-green-blue (RGB) images of real-world scenes using a modified VGG-16 neural network. We reconstructed the general light direction as azimuth and elevation angles. To avoid inaccurate results caused by coordinate uncertainty occurring at steep elevation angles, we further introduced stereographically projected coordinates. Unlike recent deep-learning-based approaches for reconstructing the light source direction, our approach does not require depth information and thus does not rely on special red-green-blue-depth (RGB-D) images as input. © 2021 University of West Bohemia. All rights reserved.

关键词： Image reconstruction

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A Research Platform for Studying Mixed-Presence Collaboration

A Research Platform for Studying Mixed-Presence Collaboratio...

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IEEE International Symposium on Mixed and Augmented Reality Workshops (ISMARW)

作者： Wolfgang Büschel Katja Krug Marc Satkowski Stefan Gumhold Raimund Dachselt Interactive Media Lab Dresden TUD Dresden University of Technology Chair of Computer Graphics and Visualization TUD Dresden University of Technology

ISBN: (数字)9798331506919

ISBN: (纸本)9798331506926

In this paper, we present a research platform to support studying collaboration in hybrid and co-located scenarios. Mixed-presence collaboration includes various novel and exciting use cases, such as situated and immersive data analysis by multiple users. However, research in this emerging field is hindered by the technical complexity of the setups and often requires re-implementation of common features. We address this issue by contributing a toolkit and research platform for mixed-presence collaboration that serves as an extensible baseline implementation and enables fast prototyping for user studies in collaborative mixed reality. Furthermore, our platform provides adjustable parameters, such as types of avatars, audio source placement, or the amount of simulated network latency. This way, developers are supported in making design choices regarding typical, re-occurring technical challenges.

关键词： Three-dimensional displays Data analysis Avatars Spatial audio Decision making Collaboration Mixed reality Complexity theory Synchronization Augmented reality

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Visualizing time-dependent key performance indicator in a graph-based analysis

Visualizing time-dependent key performance indicator in a gr...

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International Conference on Emerging Technologies and Factory Automation (ETFA)

作者： Stefan Hesse Marcel Spehr Stefan Gumhold Rainer Groh Research & Innovation SAP AG Germany TU Dresden Computer Graphics and Visualization (CGV) lab Dresden Germany Chair of Media Design TU Dresden Dresden Germany

The usage of visual analytics during the analysis of business warehouse calculated key performance indicators is one emerging challenge in modern business applications. On the one hand, a complex network of key performance indicators has to be supervised. On the other hand, within this network only few key performance indicators change obviously within a short period of time. The sole mapping of the complexity of a network of key performance indicators to a graph-based visualization only covers static information and neglects temporal dependencies. We present a new visualization approach for the enrichment of graph-based visualizations of key performance indicator networks by introducing a multi-encoded visualization of additional functional, contextual and temporal information. The should help the user to understand relationships between KPIs and alert him if something is going wrong.

关键词： visualization Correlation Data visualization Business Image color analysis Data warehouses Analytical models

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Learning to reconstruct the bubble distribution with conductivity maps using Invertible Neural Networks and Error Diffusion

arXiv

引用

arXiv 2023年

作者： Kumar, N. Krause, L. Wondrak, T. Eckert, S. Eckert, K. Gumhold, S. Chair of Computer Graphics and Visualization TU Dresden Germany Institute of Process Engineering and Environmental Technology TU Dresden Germany Institute of Fluid Dynamics Helmholtz-Zentrum Dresden-Rossendorf Dresden Germany

Electrolysis is crucial for eco-friendly hydrogen production, but gas bubbles generated during the process hinder reactions, reduce cell efficiency, and increase energy consumption. Additionally, these gas bubbles cause changes in the conductivity inside the cell, resulting in corresponding variations in the induced magnetic field around the cell. Therefore, measuring these gas bubble-induced magnetic field fluctuations using external magnetic sensors and solving the inverse problem of Biot-Savart’s Law allows for estimating the conductivity in the cell and, thus, bubble size and location. However, determining high-resolution conductivity maps from only a few induced magnetic field measurements is an ill-posed inverse problem. To overcome this, we exploit Invertible Neural Networks (INNs) to reconstruct the conductivity field. Our qualitative results and quantitative evaluation using random error diffusion show that INN achieves far superior performance compared to Tikhonov regularization. © 2023, CC BY.

关键词： Cells

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FusionINN: Decomposable Image Fusion for Brain Tumor Monitoring

arXiv

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arXiv 2024年

作者： Kumar, Nishant Tao, Ziyan Singh, Jaikirat Li, Yang Sun, Peiwen Zhao, Binghui Gumhold, Stefan Chair of Computer Graphics and Visualization Faculty of Computer Science Technische Universität Dresden Dresden Germany School of Computer Science and Engineering Shandong University of Science and Technology Qingdao China Department of Radiology Shanghai Tenth People’s Hospital Tongji University Medical School Shanghai China

Image fusion typically employs non-invertible neural networks to merge multiple source images into a single fused image. However, for clinical experts, solely relying on fused images may be insufficient for making diagnostic decisions, as the fusion mechanism blends features from source images, thereby making it difficult to interpret the underlying tumor pathology. We introduce FusionINN, a novel decomposable image fusion framework, capable of efficiently generating fused images and also decomposing them back to the source images. FusionINN is designed to be bijective by including a latent image alongside the fused image, while ensuring minimal transfer of information from the source images to the latent representation. To the best of our knowledge, we are the first to investigate the decomposability of fused images, which is particularly crucial for life-sensitive applications such as medical image fusion compared to other tasks like multi-focus or multiexposure image fusion. Our extensive experimentation validates FusionINN over existing discriminative and generative fusion methods, both subjectively and objectively. Moreover, compared to a recent denoising diffusion-based fusion model, our approach offers faster and qualitatively better fusion results. The source code of the FusionINN framework is available at: https://***/nish03/FusionINN. © 2024, CC BY.

关键词： Image fusion

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