DSpace Repository :: Browsing by Author "Ropinski, Timo"

Browsing by Author "Ropinski, Timo"

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Blue Noise Plots
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Onzenoodt, Christian van; Singh, Gurprit; Ropinski, Timo; Ritschel, Tobias; Mitra, Niloy and Viola, Ivan
We propose Blue Noise Plots, two-dimensional dot plots that depict data points of univariate data sets. While often onedimensional strip plots are used to depict such data, one of their main problems is visual clutter which results from overlap. To reduce this overlap, jitter plots were introduced, whereby an additional, non-encoding plot dimension is introduced, along which the data point representing dots are randomly perturbed. Unfortunately, this randomness can suggest non-existent clusters, and often leads to visually unappealing plots, in which overlap might still occur. To overcome these shortcomings, we introduce Blue Noise Plots where random jitter along the non-encoding plot dimension is replaced by optimizing all dots to keep a minimum distance in 2D i. e., Blue Noise. We evaluate the effectiveness as well as the aesthetics of Blue Noise Plots through both, a quantitative and a qualitative user study. The Python implementation of Blue Noise Plots is available here.
Classifier-Guided Visual Correction of Noisy Labels for Image Classification Tasks
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Bäuerle, Alex; Neumann, Heiko; Ropinski, Timo; Viola, Ivan and Gleicher, Michael and Landesberger von Antburg, Tatiana
Training data plays an essential role in modern applications of machine learning. However, gathering labeled training data is time-consuming. Therefore, labeling is often outsourced to less experienced users, or completely automated. This can introduce errors, which compromise valuable training data, and lead to suboptimal training results. We thus propose a novel approach that uses the power of pretrained classifiers to visually guide users to noisy labels, and let them interactively check error candidates, to iteratively improve the training data set. To systematically investigate training data, we propose a categorization of labeling errors into three different types, based on an analysis of potential pitfalls in label acquisition processes. For each of these types, we present approaches to detect, reason about, and resolve error candidates, as we propose measures and visual guidance techniques to support machine learning users. Our approach has been used to spot errors in well-known machine learning benchmark data sets, and we tested its usability during a user evaluation. While initially developed for images, the techniques presented in this paper are independent of the classification algorithm, and can also be extended to many other types of training data.
A Critical Analysis of the Evaluation Practice in Medical Visualization
(The Eurographics Association, 2018) Preim, Bernhard; Ropinski, Timo; Isenberg, Petra; Puig Puig, Anna and Schultz, Thomas and Vilanova, Anna and Hotz, Ingrid and Kozlikova, Barbora and Vázquez, Pere-Pau
Medical visualization aims at directly supporting physicians in diagnosis and treatment planning, students and residents in medical education, and medical physicists as well as other medical researchers in answering specific research questions. For assessing whether single medical visualization techniques or entire medical visualization systems are useful in this respect, empirical evaluations involving participants from the target user group are indispensable. The human computer interaction field developed a wide range of evaluation instruments, and the information visualization community more recently adapted and refined these instruments for evaluating (information) visualization systems. However, often medical visualization lacks behind and should pay more attention to evaluation, in particular to evaluations in realistic settings that may assess how visualization techniques contribute to cognitive activities, such as deciding about a surgical strategy or other complex treatment decisions. In this vein, evaluations that are performed over a longer period are promising to study, in order to investigate how techniques are adapted. In this paper, we discuss the evaluation practice in medical visualization based on selected examples and contrast these evaluations with the broad range of existing empirical evaluation techniques. We would like to emphasize that this paper does not serve as a general call for evaluation in medical visualization, but argues that the individual situation must be assessed and that evaluations when they are carried out should be done more carefully.
Deep-learning the Latent Space of Light Transport
(The Eurographics Association and John Wiley & Sons Ltd., 2019) Hermosilla, Pedro; Maisch, Sebastian; Ritschel, Tobias; Ropinski, Timo; Boubekeur, Tamy and Sen, Pradeep
We suggest a method to directly deep-learn light transport, i. e., the mapping from a 3D geometry-illumination-material configuration to a shaded 2D image. While many previous learning methods have employed 2D convolutional neural networks applied to images, we show for the first time that light transport can be learned directly in 3D. The benefit of 3D over 2D is, that the former can also correctly capture illumination effects related to occluded and/or semi-transparent geometry. To learn 3D light transport, we represent the 3D scene as an unstructured 3D point cloud, which is later, during rendering, projected to the 2D output image. Thus, we suggest a two-stage operator comprising a 3D network that first transforms the point cloud into a latent representation, which is later on projected to the 2D output image using a dedicated 3D-2D network in a second step. We will show that our approach results in improved quality in terms of temporal coherence while retaining most of the computational efficiency of common 2D methods. As a consequence, the proposed two stage-operator serves as a valuable extension to modern deferred shading approaches.
Enabling Viewpoint Learning through Dynamic Label Generation
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Schelling, Michael; Hermosilla, Pedro; Vázquez, Pere-Pau; Ropinski, Timo; Mitra, Niloy and Viola, Ivan
Optimal viewpoint prediction is an essential task in many computer graphics applications. Unfortunately, common viewpoint qualities suffer from two major drawbacks: dependency on clean surface meshes, which are not always available, and the lack of closed-form expressions, which requires a costly search involving rendering. To overcome these limitations we propose to separate viewpoint selection from rendering through an end-to-end learning approach, whereby we reduce the influence of the mesh quality by predicting viewpoints from unstructured point clouds instead of polygonal meshes. While this makes our approach insensitive to the mesh discretization during evaluation, it only becomes possible when resolving label ambiguities that arise in this context. Therefore, we additionally propose to incorporate the label generation into the training procedure, making the label decision adaptive to the current network predictions. We show how our proposed approach allows for learning viewpoint predictions for models from different object categories and for different viewpoint qualities. Additionally, we show that prediction times are reduced from several minutes to a fraction of a second, as compared to state-of-the-art (SOTA) viewpoint quality evaluation. Code and training data is available at https://github.com/schellmi42/viewpoint_learning, which is to our knowledge the biggest viewpoint quality dataset available.
EUROGRAPHICS 2017: Dirk Bartz Prize Frontmatter
(Eurographics Association, 2017) Bruckner, Stefan; Ropinski, Timo;
EuroVis 2017: Frontmatter
(Eurographics Association, 2017) Heer, Jeffrey; Ropinski, Timo; van Wijk, Jarke;
Improving Perception of Molecular Surface Visualizations by Incorporating Translucency Effects
(The Eurographics Association, 2018) Hermosilla, Pedro; Maisch, Sebastian; Vázquez, Pere-Pau; Ropinski, Timo; Puig Puig, Anna and Schultz, Thomas and Vilanova, Anna and Hotz, Ingrid and Kozlikova, Barbora and Vázquez, Pere-Pau
Molecular surfaces are a commonly used representation in the analysis of molecular structures as they provide a compact description of the space occupied by a molecule and its accessibility. However, due to the high abstraction of the atomic data, fine grain features are hard to identify. Moreover, these representations involve a high degree of occlusions, which prevents the identification of internal features and potentially impacts shape perception. In this paper, we present a set of techniques which are inspired by the properties of translucent materials, that have been developed to improve the perception of molecular surfaces: First, we introduce an interactive algorithm to simulate subsurface scattering for molecular surfaces, in order to improve the thickness perception of the molecule. Second, we present a technique to visualize structures just beneath the surface, by still conveying relevant depth information. And lastly, we introduce reflections and refractions into our visualization that improve the shape perception of molecular surfaces. We evaluate the benefits of these methods through crowd-sourced user studies as well as the feedback from several domain experts.
Interactive Subsurface Scattering for Materials With High Scattering Distances
(© 2020 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2020) Maisch, Sebastian; Ropinski, Timo; Benes, Bedrich and Hauser, Helwig
Existing algorithms for rendering subsurface scattering in real time cannot deal well with scattering over longer distances. Kernels for image space algorithms become very large in these circumstances and separation does not work anymore, while geometry‐based algorithms cannot preserve details very well. We present a novel approach that deals with all these downsides. While for lower scattering distances, the advantages of geometry‐based methods are small, this is not the case anymore for high scattering distances (as we will show). Our proposed method takes advantage of the highly detailed results of image space algorithms and combines it with a geometry‐based method to add the essential scattering from sources not included in image space. Our algorithm does not require pre‐computation based on the scene's geometry, it can be applied to static and animated objects directly. Our method is able to provide results that come close to ray‐traced images which we will show in direct comparisons with images generated by PBRT. We will compare our results to state of the art techniques that are applicable in these scenarios and will show that we provide superior image quality while maintaining interactive rendering times.
Property-Based Testing for Visualization Development
(The Eurographics Association, 2021) Stegmaier, Michael; Engel, Dominik; Olbrich, Jannik; Ropinski, Timo; Tichy, Matthias; Gillmann, Christina and Krone, Michael and Reina, Guido and Wischgoll, Thomas
As the testing capabilities of current visualization software fail to cover a large space of rendering parameters, we propose to use property-based testing to automatically generate a large set of tests with different parameter sets. By comparing the resulting renderings for pairs of different parameters, we can verify certain effects to be expected in the rendering upon change of a specific parameter. This allows for testing visualization algorithms with a large coverage of rendering parameters. Our proposed approach can also be used in a test-driven manner, meaning the tests can be defined alongside the actual algorithm. Lastly, we show that by integrating the proposed concepts into the existing regression testing pipeline of Inviwo, we can execute the property-based testing process in a continuous integration setup. To demonstrate our approach, we describe use cases where property-based testing can help to find errors during visualization development.
Real-Time Visualization of 3D Amyloid-Beta Fibrils from 2D Cryo-EM Density Maps
(The Eurographics Association, 2020) Kniesel, Hannah; Ropinski, Timo; Hermosilla, Pedro; Kozlíková, Barbora and Krone, Michael and Smit, Noeska and Nieselt, Kay and Raidou, Renata Georgia
Amyloid-beta fibrils are the result of the accumulation of misfolded amyloid precursor proteins along an axis. These fibrils play a crucial role in the development of Alzheimer's disease, and yet its creation and structure are not fully understood. Visualization is often used to understand the structure of such fibrils. Unfortunately, existing algorithms require high memory consumption limiting their applications. In this paper, we introduce a ray marching algorithm that takes advantage of the inherent repetition in these atomic structures, requiring only a 2D density map to represent the fibril. During ray marching, the texture coordinates are transformed based on the position of the sample along the longitudinal axis, simulating the rotation of the fibrils. Our algorithm reduces memory consumption by a large margin and improves GPU cache hits, making it suitable for real-time visualizations. Moreover, we present several shading algorithms for this type of data, such as shadows or ambient occlusion, in order to improve perception. Lastly, we provide a simple yet effective algorithm to communicate the uncertainty introduced during reconstruction. During the evaluation process, we were able to show, that our approach not only outperforms the Standard Volume Rendering method by significantly lower memory consumption and high image quality for low resolution 2D density maps but also in performance.
Semantic Hierarchical Exploration of Large Image Datasets
(The Eurographics Association, 2023) Bäuerle, Alex; Onzenoodt, Christian van; Jönsson, Daniel; Ropinski, Timo; Hoellt, Thomas; Aigner, Wolfgang; Wang, Bei
We present a method for exploring and comparing large sets of images with metadata using a hierarchical interaction approach. Browsing many images at the same time requires either a large screen space or an abundance of scrolling interaction. We address this problem by projecting the images onto a two-dimensional Cartesian coordinate system by combining the latent space of vision neural networks and dimensionality reduction techniques. To alleviate overdraw of the images, we integrate a hierarchical layout and navigation, where each group of similar images is represented by the image closest to the group center. Advanced interactive analysis of images in relation to their metadata is enabled through integrated, flexible filtering based on expressions. Furthermore, groups of images can be compared through selection and automated aggregated metadata visualization. We showcase our method in three case studies involving the domains of photography, machine learning, and medical imaging.
A Visualization‐Based Analysis System for Urban Search & Rescue Mission Planning Support
(© 2017 The Eurographics Association and John Wiley & Sons Ltd., 2017) Bock, Alexander; Svensson, Åsa; Kleiner, Alexander; Lundberg, Jonas; Ropinski, Timo; Chen, Min and Zhang, Hao (Richard)
We propose a visualization system for incident commanders (ICs) in urban search and rescue scenarios that supports path planning in post‐disaster structures. Utilizing point cloud data acquired from unmanned robots, we provide methods for the assessment of automatically generated paths. As data uncertainty and unknown information make fully automated systems impractical, we present the IC with a set of viable access paths, based on varying risk factors, in a 3D environment combined with visual analysis tools enabling informed decision making and trade‐offs. Based on these decisions, a responder is guided along the path by the IC, who can interactively annotate and reevaluate the acquired point cloud and generated paths to react to the dynamics of the situation. We describe visualization design considerations for our system and decision support systems in general, technical realizations of the visualization components, and discuss the results of two qualitative expert evaluation; one online study with nine search and rescue experts and an eye‐tracking study in which four experts used the system on an application case.We propose a visualization system for incident commanders (ICs) in urban search and rescue scenarios that supports path planning in post‐disaster structures. Utilizing point cloud data acquired from unmanned robots, we provide methods for the assessment of automatically generated paths.
Where did my Lines go? Visualizing Missing Data in Parallel Coordinates
(The Eurographics Association and John Wiley & Sons Ltd., 2022) Bäuerle, Alex; Onzenoodt, Christian van; Kinderen, Simon der; Westberg, Jimmy Johansson; Jönsson, Daniel; Ropinski, Timo; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
We evaluate visualization concepts to represent missing values in parallel coordinates. We focus on the trade-off between the ability to perceive missing values and the concept's impact on common tasks. For this purpose, we identified three missing value representation concepts: removing line segments where values are missing, adding a separate, horizontal axis onto which missing values are projected, and using imputed values as a replacement for missing values. For the missing values axis and imputed values concepts, we additionally add downplay and highlight variations. We performed a crowd-sourced, quantitative user study with 732 participants comparing the concepts and their variations using five real-world datasets. Based on our findings, we provide suggestions regarding which visual encoding to employ depending on the task at focus.

Browsing by Author "Ropinski, Timo"

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