VMV19

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https://diglib.eg.org/handle/10.2312/2632811

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A Visual Analytics Tool for Cohorts in Motion Data
(The Eurographics Association, 2019) Sheharyar, Ali; Ruh, Alexander; Valkov, Dimitar; Markl, Michael; Bouhali, Othmane; Linsen, Lars; Schulz, Hans-Jörg and Teschner, Matthias and Wimmer, Michael
Motion data are curves over time in a 1D, 2D, or 3D space. To analyze sets of curves, machine learning methods can be applied to cluster them and detect outliers. However, often metadata or prior knowledge of the analyst drives the analysis by defining cohorts. Our goal is to provide a flexible system for comparative visual analytics of cohorts in motion data. The analyst interactively defines cohorts by filtering on metadata properties. We, then, apply machine learning and statistical methods to extract the main features of each cohort. Summarizations of these features are visually encoded using, in particular, boxplots and their extensions to functional and curve boxplots, depending on the number of selected dimensions of the space. These summarizations allow for an intuitive comparative visual analysis of cohorts in a juxtaposed or superimposed representation. Our system provides full flexibility in defining cohorts, selecting time intervals and spatial dimensions, and adjusting the aggregation level of summarizations. Comparison of an individual sample against a cohort is also supported. We demonstrate the functionality, effectiveness, and flexibility of our system by applying it to a range of diverse motion data sets.
Reconfigurable Snapshot HDR Imaging Using Coded Masks and Inception Network
(The Eurographics Association, 2019) Alghamdi, Masheal; Fu, Qiang; Thabet, Ali; Heidrich, Wolfgang ; Schulz, Hans-Jörg and Teschner, Matthias and Wimmer, Michael
High Dynamic Range (HDR) image acquisition from a single image capture, also known as snapshot HDR imaging, is challenging because the bit depths of camera sensors are far from sufficient to cover the full dynamic range of the scene. Existing HDR techniques focus either on algorithmic reconstruction or hardware modification to extend the dynamic range. In this paper we propose a joint design for snapshot HDR imaging by devising a spatially-varying modulation mask in the hardware as well as building an inception network to reconstruct the HDR image. We achieve a reconfigurable HDR camera design that does not require custom sensors, and instead can be reconfigured between HDR and conventional mode with very simple calibration steps. We demonstrate that the proposed hardware-software solution offers a flexible yet robust way to modulating per-pixel exposures, and the network requires little knowledge of the hardware to faithfully reconstruct the HDR image. Comparison results show that our method outperforms state of the art in terms of visual perception quality.
Learning a Perceptual Quality Metric for Correlation in Scatterplots
(The Eurographics Association, 2019) Wöhler, Leslie; Zou, Yuxin; Mühlhausen, Moritz; Albuquerque, Georgia; Magnor, Marcus; Schulz, Hans-Jörg and Teschner, Matthias and Wimmer, Michael
Visual quality metrics describe the quality and efficiency of multidimensional data visualizations in order to guide data analysts during exploration tasks. Current metrics are usually based on empirical algorithms which do not accurately represent human perception and therefore often differ from the analysts' expectations. We propose a new perception-based quality metric using deep learning that rates the correlation of data dimensions visualized by scatterplots. First, we created a data set containing over 15,000 pairs of scatterplots with human annotations on the perceived correlation between the data dimensions. Afterwards, we trained two different Convolutional Neural Networks (CNN), one extracts features from scatterplot images and the other directly from data vectors. We evaluated both CNNs on our test set and compared them to previous visual quality metrics. The experiments show that our new metric is able to represent human perception more accurately than previous methods.
Visualizing Transport and Mixing in Particle-based Fluid Flows
(The Eurographics Association, 2019) Rapp, Tobias; Dachsbacher, Carsten; Schulz, Hans-Jörg and Teschner, Matthias and Wimmer, Michael
To gain insight into large, time-dependent particle-based fluid flows, we visually analyze Lagrangian coherent structures (LCS), a robust skeleton of the underlying particle dynamics. To identify these coherent structures, we build on recent work that efficiently computes the finite-time Lyapunov exponent (FTLE) directly on particle data. We formulate the LCS definitions for particles based on robust approximations for higher-order derivatives of the FTLE. Based on these formulations, we derive a per-particle distance to the closest coherent structure. This allows us to visually analyze and explore the Lagrangian transport behavior directly on the particle data. We show that this is especially beneficial to detect and visualize flow features on different time scales. Lastly, we apply our approach to study mixing in multiphase flows by visualizing the different types of fluids and their relation to the coherent structures.
Visual Analytics of Simulation Ensembles for Network Dynamics
(The Eurographics Association, 2019) Ngo, Quynh Quang; Hütt, Marc-Thorsten; Linsen, Lars; Schulz, Hans-Jörg and Teschner, Matthias and Wimmer, Michael
A central question in the field of Network Science is to analyze the role of a given network topology on the dynamical behavior captured by time-varying simulations executed on the network. These dynamical systems are also influenced by global simulation parameters. We present a visual analytics approach that supports the investigation of the impact of the parameter settings, i.e., how parameter choices change the role of network topology on the simulations' dynamics. To answer this question, we are analyzing ensembles of simulation runs with different parameter settings executed on a given network topology. We relate the nodes' topological structures to their dynamical similarity in a 2D plot based on an interactively defined hierarchy of topological properties and a 1D embedding for the dynamical similarity. We evaluate interactively defined topological groups with respect to matching dynamical behavior, which we visually encode as graphs of the function of the considered simulation parameter. Interactive filtering and coordinated views allow for a detailed analysis of the parameter space with respect to topology-dynamics relations. Our visual analytics approach is applied to scenarios for excitable dynamics on synthetic and real brain connectome networks.
Clustering Ensembles of 3D Jet-Stream Core Lines
(The Eurographics Association, 2019) Kern, Michael; Westermann, Rüdiger; Schulz, Hans-Jörg and Teschner, Matthias and Wimmer, Michael
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.
VMV 2019: Frontmatter
(Eurographics Association, 2019) Schulz, Hans-Jörg; Teschner, Matthias; Wimmer, Michael; Schulz, Hans-Jörg and Teschner, Matthias and Wimmer, Michael
Normal Map Bias Reduction for Many-Lights Multi-View Photometric Stereo
(The Eurographics Association, 2019) Gan, Jiangbin; Bergen, Philipp; Thormählen, Thorsten; Drescher, Philip; Hagens, Ralf; Schulz, Hans-Jörg and Teschner, Matthias and Wimmer, Michael
In this paper, we improve upon an existing many-lights multi-view photometric stereo approach. Firstly, we show how to detect continuous regions for normal integration, which leads to a fully automatic reconstruction pipeline. Secondly, we compute perpixel light source visibilities using an initial biased reconstruction in order to update the estimated normal map to a solution with reduced bias. Thirdly, to further improve the normal accuracy, we compensate for interreflections of light between surface locations. Our approach is evaluated on both synthetic and real-world data and it is shown that the normal accuracy is improved by around 50 percent.
Polarization Demosaicking for Monochrome and Color Polarization Focal Plane Arrays
(The Eurographics Association, 2019) Qiu, Simeng; Fu, Qiang; Wang, Congli; Heidrich, Wolfgang ; Schulz, Hans-Jörg and Teschner, Matthias and Wimmer, Michael
Division-of-focal-plane (DoFP) polarization image sensors allow for snapshot imaging of linear polarization effects with inexpensive and straightforward setups. However, conventional interpolation based image reconstruction methods for such sensors produce unreliable and noisy estimates of quantities such as degree of linear polarization (DoLP) or angle of linear polarization (AoLP). In this paper, we propose a polarization demosaicking algorithm by inverting the polarization image formation model for both monochrome and color DoFP cameras. Compared to previous interpolation methods, our approach can significantly reduce noise induced artifacts and drastically increase the accuracy in estimating polarization states. We evaluate and demonstrate the performance of the methods on a new high-resolution color polarization dataset. Simulation and experimental results show that the proposed reconstruction and analysis tools offer an effective solution to polarization imaging.
Open-Box Training of Kernel Support Vector Machines: Opportunities and Limitations
(The Eurographics Association, 2019) Khatami, Mohammad; Schultz, Thomas; Schulz, Hans-Jörg and Teschner, Matthias and Wimmer, Michael
Kernel Support Vector Machines (SVMs) are widely used for supervised classification, and have achieved state-of-the-art performance in numerous applications. We aim to further increase their efficacy by allowing a human operator to steer their training process. To this end, we identify several possible strategies for meaningful human intervention in their training, propose a corresponding visual analytics workflow, and implement it in a prototype system. Initial results from two users, on data from three different domains suggest that, in addition to facilitating better insight into the data and into the classifier's decision process, visual analytics can increase the efficacy of Support Vector Machines when the data available for training has a low number of samples, is unbalanced with respect to the different classes, contains outliers, irrelevant features, or mislabeled samples. However, we also discuss some limitations of improving the efficacy of supervised classification with visual analytics.
Stochastic Convolutional Sparse Coding
(The Eurographics Association, 2019) Xiong, Jinhui; Richtarik, Peter; Heidrich, Wolfgang ; Schulz, Hans-Jörg and Teschner, Matthias and Wimmer, Michael
State-of-the-art methods for Convolutional Sparse Coding usually employ Fourier-domain solvers in order to speed up the convolution operators. However, this approach is not without shortcomings. For example, Fourier-domain representations implicitly assume circular boundary conditions and make it hard to fully exploit the sparsity of the problem as well as the small spatial support of the filters. In this work, we propose a novel stochastic spatial-domain solver, in which a randomized subsampling strategy is introduced during the learning sparse codes. Afterwards, we extend the proposed strategy in conjunction with online learning, scaling the CSC model up to very large sample sizes. In both cases, we show experimentally that the proposed subsampling strategy, with a reasonable selection of the subsampling rate, outperforms the state-of-the-art frequency-domain solvers in terms of execution time without losing the learning quality. Finally, we evaluate the effectiveness of the over-complete dictionary learned from large-scale datasets, which demonstrates an improved sparse representation of the natural images on account of more abundant learned image features.
Cluster-based Analysis of Multi-Parameter Distributions in Cloud Simulation Ensembles
(The Eurographics Association, 2019) Kumpf, Alexander; Stumpfegger, Josef; Westermann, Rüdiger; Schulz, Hans-Jörg and Teschner, Matthias and Wimmer, Michael
The proposed approach enables a comparative visual exploration of multi-parameter distributions in time-varying 3D ensemble simulations. To investigate whether dominant trends in such distributions occur, we consider the simulation elements in each dataset-per ensemble member and time step-as elements in the multi-dimensional parameter space, and use t-SNE to project these elements into 2D space. To find groups of elements with similar parameter values in each time step, the resulting projections are clustered via k-Means. Since elements with similar data values can be disconnected in one single projection, we compute an ensemble of projections using multiple t-SNE runs and use evidence accumulation to determine sets of elements that are stably clustered together. We build upon per-cluster multi-parameter distribution functions to quantify cluster similarity, and merge clusters in different ensemble members. By applying the proposed approach to a time-varying ensemble, the temporal development of clusters, and in particular their stability over time can be analyzed. We apply this approach to analyze a time-varying ensemble of 3D cloud simulations. The visualizations via t-SNE, parallel coordinate plots and scatter plot matrices show dependencies between the simulation results and the simulation parameters used to generate the ensemble, and they provide insight into the temporal ensemble variability regarding the major trends in the multi-parameter distributions.
Local Remote Photoplethysmography Signal Analysis for Application in Presentation Attack Detection
(The Eurographics Association, 2019) Kossack, Benjamin; Wisotzky, Eric L.; Hilsmann, Anna; Eisert, Peter; Schulz, Hans-Jörg and Teschner, Matthias and Wimmer, Michael
This paper presents a method to analyze and visualize the local blood flow through human skin tissue within the face and neck. The method is based on the local signal characteristics and extracts and analyses the local propagation of blood flow from video recordings. In a first step, the global pulse rate is identified in RGB images using normalized green color channel intensities. We then calculate for an image sequence, a local remote photoplethysmography (rPPG) signal that is presented by a chrominancebased signal. This local rPPG signal is analyzed and then used to extract the local blood flow propagation from signal-to-noise ratio (SNR) and pulse transit time (PTT) maps. These maps are used to visualize the propagation of the blood flow (PTT) and reveal the signal quality of each spatial position (SNR). We further proved a novel pulse rate based skin segmentation method, that is based on the global pulse rate and the statistical properties of the SNR map. This skin segmentation method allows a direct application in liveliness detection, e.g., for presentation attack detection (PAD). Based on the described local blood flow analysis, we propose a PAD system, that specializes in identifying a partial face and neck coverage in the video. The system is tested using datasets showing a person with different facial coverings, such as a mask or a thick layer of makeup. All tested masks can be detected and identified as presentation attacks.
Consistent Filtering of Videos and Dense Light-Fields Without Optic-Flow
(The Eurographics Association, 2019) Shekhar, Sumit; Semmo, Amir; Trapp, Matthias; Tursun, Okan; Pasewaldt, Sebastian; Myszkowski, Karol; Döllner, Jürgen; Schulz, Hans-Jörg and Teschner, Matthias and Wimmer, Michael
A convenient post-production video processing approach is to apply image filters on a per-frame basis. This allows the flexibility of extending image filters-originally designed for still images-to videos. However, per-image filtering may lead to temporal inconsistencies perceived as unpleasant flickering artifacts, which is also the case for dense light-fields due to angular inconsistencies. In this work, we present a method for consistent filtering of videos and dense light-fields that addresses these problems. Our assumption is that inconsistencies-due to per-image filtering-are represented as noise across the image sequence. We thus perform denoising across the filtered image sequence and combine per-image filtered results with their denoised versions. At this, we use saliency based optimization weights to produce a consistent output while preserving the details simultaneously. To control the degree-of-consistency in the final output, we implemented our approach in an interactive real-time processing framework. Unlike state-of-the-art inconsistency removal techniques, our approach does not rely on optic-flow for enforcing coherence. Comparisons and a qualitative evaluation indicate that our method provides better results over state-of-the-art approaches for certain types of filters and applications.
Multi-Level-Memory Structures for Adaptive SPH Simulations
(The Eurographics Association, 2019) Winchenbach, Rene; Kolb, Andreas; Schulz, Hans-Jörg and Teschner, Matthias and Wimmer, Michael
In this paper we introduce a novel hash map-based sparse data structure for highly adaptive Smoothed Particle Hydrodynamics (SPH) simulations on GPUs. Our multi-level-memory structure is based on stacking multiple independent data structures, which can be created efficiently from the same particle data by utilizing self-similar particle orderings. Furthermore, we propose three neighbor list algorithms that improve performance, or significantly reduce memory requirements, when compared to Verlet-lists for the overall simulation. Overall, our proposed method significantly improves the performance of spatially adaptive methods, allows for the simulation of unbounded domains and reduces memory requirements without interfering with the simulation.
Visual Analysis of Probabilistic Infection Contagion in Hospitals
(The Eurographics Association, 2019) Wunderlich, Marcel; Block, Isabelle; von Landesberger, Tatiana; Petzold, Markus; Marschollek, Michael; Scheithauer, Simone; Schulz, Hans-Jörg and Teschner, Matthias and Wimmer, Michael
Clinicians and hygienists need to know how an infection of one patient could be transmitted among other patients in the hospital (e.g., to prevent outbreaks). They need to analyze how many and which patients will possibly be infected, how fast the infection could spread, and which contacts are likely to transfer the infections within the hospital. Currently, infection contagion is modeled and visualized for populations only on an aggregate level, without identification and exploration of possible infection between individuals. We present a novel visual analytics approach that simulates the contagion in a contact graph of patients in a hospital. We propose a clustering approach to identify probable contagion scenarios in the simulation ensemble. Furthermore, our novel visual design for detailed assessment of transmission shows the temporal development of contagion per patient in one view. We demonstrate the capability of our approach to a real-world use case in a German hospital.
RodMesh: Two-handed 3D Surface Modeling in Virtual Reality
(The Eurographics Association, 2019) Verhoeven, Floor; Sorkine-Hornung, Olga; Schulz, Hans-Jörg and Teschner, Matthias and Wimmer, Michael
User interfaces for 3D shape modeling in Virtual Reality (VR), unlike basic tasks such as text input and item selection, have been less explored in research so far. Shape modeling in 3D lends itself very well to VR, since the 3D immersion provides the user with richer spatial feedback and depth perception when compared to traditional 2D displays. That said, currently existing 3D modeling applications do not focus on optimizing the modeling interaction techniques for VR, but instead mostly merely port standard interaction paradigms. Our approach utilizes a popular sketch-based surface modeling algorithm in VR by rethinking the user interface in order to benefit from the 3D immersive environment and its inherent support of two-handed input. We propose a bimanual interaction technique that allows users to create 3D models via virtual deformable rods. These rods are bendable into outline shapes that are automatically inflated into manifold mesh surfaces, and can then be incrementally edited to further refine the shape.
Trigonometric Moments for Editable Structured Light Range Finding
(The Eurographics Association, 2019) Werner, Sebastian; Iseringhausen, Julian; Callenberg, Clara; Hullin, Matthias; Schulz, Hans-Jörg and Teschner, Matthias and Wimmer, Michael
Structured-light methods remain one of the leading technologies in high quality 3D scanning, specifically for the acquisition of single objects and simple scenes. For more complex scene geometries, however, non-local light transport (e.g. interreflections, sub-surface scattering) comes into play, which leads to errors in the depth estimation. Probing the light transport tensor, which describes the global mapping between illumination and observed intensity under the influence of the scene can help to understand and correct these errors, but requires extensive scanning. We aim to recover a 3D subset of the full 4D light transport tensor, which represents the scene as illuminated by line patterns, rendering the approach especially useful for triangulation methods. To this end we propose a frequency-domain approach based on spectral estimation to reduce the number of required input images. Our method can be applied independently on each pixel of the observing camera, making it perfectly parallelizable with respect to the camera pixels. The result is a closed-form representation of the scene reflection recorded under line illumination, which, if necessary, masks pixels with complex global light transport contributions and, if possible, enables the correction of such measurements via data-driven semi-automatic editing.
Reflection Symmetry in Textured Sewing Patterns
(The Eurographics Association, 2019) Wolff, Katja; Herholz, Philipp; Sorkine-Hornung, Olga; Schulz, Hans-Jörg and Teschner, Matthias and Wimmer, Michael
Recent work in the area of digital fabrication of clothes focuses on repetitive print patterns, specifically the 17 wallpaper groups, and their alignment along garment seams. While adjusting the underlying sewing patterns for maximized fit of wallpapers along seams, past research does not account for global symmetries that underlie almost every sewing pattern due to the symmetry of the human body. We propose an interactive tool to define such symmetries and integrate them into the existing algorithm, such that both the texture alignment and the deformation of the sewing pattern adhere to these symmetries.
Joint Schedule and Layout Autotuning for Sparse Matrices with Compound Entries on GPUs
(The Eurographics Association, 2019) Mueller-Roemer, Johannes Sebastian; Stork, André; Fellner, Dieter W.; Schulz, Hans-Jörg and Teschner, Matthias and Wimmer, Michael
Large sparse matrices with compound entries, i.e., complex and quaternionic matrices as well as matrices with dense blocks, are a core component of many algorithms in geometry processing, physically based animation, and other areas of computer graphics. We generalize several matrix layouts and apply joint schedule and layout autotuning to improve the performance of the sparse matrix-vector product on massively parallel graphics processing units. Compared to schedule tuning without layout tuning, we achieve speedups of up to 5.5x. In comparison to cuSPARSE, we achieve speedups of up to 4.7x

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