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Real-time rendering of complex photorealistic landscapes using hybrid level-of-detail approaches
(2005)
Einführung: Die Tiefenwirkung dreidimensionaler Räume in einem zweidimensionalen Bild einzufangen, ist ein Faszinosum nahezu aller Kulturen der Menschheitsgeschichte. Der vorliegende Aufsatz folgt den Spuren dieses Faszinosums, vergleichend in der Malerei und der mathematisierten Computergrafik. Die Entdeckung der Zentralperspektive in der italienischen Renaissance zeigt bereits den engen Zusammenhang von Malerei und Mathematik. Auf der Suche nach Maltechniken, mit denen Raumtiefe bildnerisch dargestellt werden kann, beginnen wir in Kap. 2 mit einem chronologischen Gang durch verschiedene Epochen der europäischen Malerei. Hieraus abgeleitete Prinzipien, soweit sie im Rechner realisierbar scheinen, stellen wir in Kap. 3 am Beispiel moderner Methoden der mathematischen Visualisierung vor.
Visualization grammars are gaining popularity as they allow visualization specialists and experienced users to quickly create static and interactive views. Existing grammars, however, mostly focus on abstract views, ignoring three-dimensional (3D) views, which are very important in fields such as natural sciences. We propose a generalized interaction grammar for the problem of coordinating heterogeneous view types, such as standard charts (e.g., based on Vega-Lite) and 3D anatomical views. An important aspect of our web-based framework is that user interactions with data items at various levels of detail can be systematically integrated and used to control the overall layout of the application workspace. With the help of a concise JSON-based specification of the intended workflow, we can handle complex interactive visual analysis scenarios. This enables rapid prototyping and iterative refinement of the visual analysis tool in collaboration with domain experts. We illustrate the usefulness of our framework in two real-world case studies from the field of neuroscience. Since the logic of the presented grammar-based approach for handling interactions between heterogeneous web-based views is free of any application specifics, it can also serve as a template for applications beyond biological research.
To improve existing weather prediction and reanalysis capabilities, high-resolution and multi-modal climate data becomes an increasingly important topic. The advent of increasingly dense numerical simulation of atmospheric phenomena, provides new means to better understand dynamic processes and to visualize structural flow patterns that remain hidden otherwise. In the presented illustrations we demonstrate an advanced technique to visualize multiple scales of dense flow fields and Lagrangian patterns therein, simulated by state-of-the-art simulation models for each scale. They provide a deeper insight into the structural differences and patterns that occur on each scale and highlight the complexity of flow phenomena in our atmosphere.
To improve existing weather prediction and reanalysis capabilities, high-resolution and multi-modal climate data becomes an increasingly important topic. The advent of increasingly dense numerical simulation of atmospheric phenomena, provides new means to better understand dynamic processes and to visualize structural flow patterns that remain hidden otherwise. In the presented illustrations we demonstrate an advanced technique to visualize multiple scales of dense flow fields and Lagrangian patterns therein, simulated by state-of-the-art simulation models for each scale. They provide a deeper insight into the structural differences and patterns that occur on each scale and highlight the complexity of flow phenomena in our atmosphere.
This paper is associated with a poster winner of a 2016 APS/DFD Milton van Dyke Award for work presented at the DFD Gallery of Fluid Motion. The original poster is available from the Gallery of Fluid Motion, https://doi.org/10.1103/APS.DFD.2016.GFM.P0030
Probabilistic Marching Cubes
(2011)
The goal of visualization is to effectively and accurately communicate data. Visualization research has often overlooked the errors and uncertainty which accompany the scientific process and describe key characteristics used to fully understand the data. The lack of these representations can be attributed, in part, to the inherent difficulty in defining, characterizing, and controlling this uncertainty, and in part, to the difficulty in including additional visual metaphors in a well designed, potent display. However, the exclusion of this information cripples the use of visualization as a decision making tool due to the fact that the display is no longer a true representation of the data. This systematic omission of uncertainty commands fundamental research within the visualization community to address, integrate, and expect uncertainty information. In this chapter, we outline sources and models of uncertainty, give an overview of the state-of-the-art, provide general guidelines, outline small exemplary applications, and finally, discuss open problems in uncertainty visualization.
Intakte Gelenke sind eine Voraussetzung für das Funktionieren des Skeletts und die Mobilität im Lebensalltag. Ein gesunder Bewegungsapparat ist die Grundlage für die Funktionsfähigkeit des Herz-Kreislauf-Systems wie auch der Immunabwehr. Bewegungs- und Physiotherapie sowie verschiedene Formen der Patientenaktivität stellen essenzielle klinische Ansätze in der Behandlung von neurodegenerativen Erkrankungen, Schlaganfall, Diabetes und Krebs dar. Kommt es zu degenerativen Veränderungen von Gelenken, bedeutet dies eine wesentliche Beeinträchtigung der Mobilität. Nächtliche Schmerzen und Schlafstörungen treten in fortgeschrittenen Stadien auf und sind besonders belastend. Arthrose wird auch als degenerative Gelenkerkrankung bezeichnet. Sie geht mit Veränderungen in der Struktur und Zusammensetzung des Gelenkknorpels wie auch des verkalkten Knorpels, der subchondralen Kortikalis, der subchondralen Spongiosa, des Meniskus, der Gelenkkapsel und der Synovialis einher, was schließlich zur Degeneration dieser Gewebe führt, aus denen sich die Synovialgelenke zusammensetzen.
Intact joints are necessary for skeletal function and mobility in daily life. A healthy musculoskeletal system is the basis for a functional cardiovascular
system as well as an intact immune system. Locomotion, physiotherapy, and various forms of patient activity are essential clinical therapies used in the treatment of neurodegeneration, stroke, diabetes, and cancer. Mobility is substantially impaired with degeneration of joints and, in advanced stages, nighttime pain and sleep disturbance are particularly cumbersome.
Osteoarthritis (OA) is also known as degenerative joint disease. OA involves structural and compositional changes in the articular cartilage, as well as in the calcified cartilage, subchondral cortical bone, subchondral cancellous bone, meniscus, joint capsular tissue, and synovium; which eventually lead to degeneration of these tissues comprising synovial joints.
ORBKIT is a toolbox for postprocessing electronic structure calculations based on a highly modular and portable Python architecture. The program allows computing a multitude of electronic properties of molecular systems on arbitrary spatial grids from the basis set representation of its electronic wave function, as well as several grid-independent properties. The required data can be extracted directly from the standard output of a large number of quantum chemistry programs. ORBKIT can be used as a standalone program to determine standard quantities, for example, the electron density, molecular orbitals, and derivatives thereof. The cornerstone of ORBKIT is its modular structure. The existing basic functions can be arranged in an individual way and can be easily extended by user-written modules to determine any other derived quantity. ORBKIT offers multiple output formats that can be processed by common visualization tools (VMD, Molden, etc.). Additionally, ORBKIT offers routines to order molecular orbitals computed at different nuclear configurations according to their electronic character and to interpolate the wavefunction between these configurations. The program is open-source under GNU-LGPLv3 license and freely available at https://github.com/orbkit/orbkit/.
This article provides an overview of ORBKIT with particular focus
on its capabilities and applicability, and includes several example
calculations.
Operieren im Medizin-Grid
(2002)
Recent advances in high-resolution, cloud resolving simulation models pose several challenges towards respective analysis methodologies. To enable efficient comparison and validation of such models efficient, scalable, and informative diagnostic procedures are mandatory. In this talk, an object-based evaluation scheme based on the notion of scalar field topology will be presented. The presentation will cover the application of topological clustering procedures for object identification, tracking, and the retrieval of object-based statistics. The pro-posed methodology is shown to enable an advanced in-depth evaluation and visualization of high cloud-resolving models. Using a newly developed large-scale high-resolution model (i.e., HD(CP)2 ICON), it will be demonstrated that the presented procedures are applicable to assess the model performance compared to measurements (e.g., radar, satellite) and standard operational models (COSMO) at different domains and spatial scales.
Intrinsic and parametric regression models are of high interest for the statistical analysis of manifold-valued data such as images and shapes. The standard linear ansatz has been generalized to geodesic regression on manifolds making it possible to analyze dependencies of random variables that spread along generalized straight lines. Nevertheless, in some scenarios, the evolution of the data cannot be modeled adequately by a geodesic.
We present a framework for nonlinear regression on manifolds by considering Riemannian splines, whose segments are Bézier curves, as trajectories.
Unlike variational formulations that require time-discretization, we take a constructive approach that provides efficient and exact evaluation by virtue of the generalized de Casteljau algorithm.
We validate our method in experiments on the reconstruction of periodic motion of the mitral valve as well as the analysis of femoral shape changes during the course of osteoarthritis, endorsing Bézier spline regression as an effective and flexible tool for manifold-valued regression.
Neue Bilder für die Medizin?
(2005)
Background
Geometric parameters have been proposed for prediction of cerebral aneurysm rupture risk. Predicting the rupture risk for incidentally detected unruptured aneurysms could help clinicians in their treatment decision. However, assessment of geometric parameters depends on several factors, including the spatial resolution of the imaging modality used and the chosen reconstruction procedure. The aim of this study was to investigate the uncertainty of a variety of previously proposed geometric parameters for rupture risk assessment, caused by variability of reconstruction procedures.
Materials
26 research groups provided segmentations and surface reconstructions of five cerebral aneurysms as part of the Multiple Aneurysms AnaTomy CHallenge (MATCH) 2018. 40 dimensional and non-dimensional geometric parameters, describing aneurysm size, neck size, and irregularity of aneurysm shape, were computed. The medians as well as the absolute and relative uncertainties of the parameters were calculated. Additionally, linear regression analysis was performed on the absolute uncertainties and the median parameter values.
Results
A large variability of relative uncertainties in the range between 3.9 and 179.8% was found. Linear regression analysis indicates that some parameters capture similar geometric aspects. The lowest uncertainties < 6% were found for the non-dimensional parameters isoperimetric ratio, convexity ratio, and ellipticity index. Uncertainty of 2D and 3D size parameters was significantly higher than uncertainty of 1D parameters. The most extreme uncertainties > 80% were found for some curvature parameters.
Conclusions
Uncertainty analysis is essential on the road to clinical translation and use of rupture risk prediction models. Uncertainty quantification of geometric rupture risk parameters provided by this study may help support development of future rupture risk prediction models.
Studying neural mechanisms in complementary model organisms from different ecological niches in the same animal class can leverage the comparative brain analysis at the cellular level. To advance such a direction, we developed a unified brain atlas platform and specialized tools that allowed us to quantitatively compare neural structures in two teleost larvae, medaka (Oryzias latipes) and zebrafish (Danio rerio). Leveraging this quantitative approach we found that most brain regions are similar but some subpopulations are unique in each species. Specifically, we confirmed the existence of a clear dorsal pallial region in the telencephalon in medaka lacking in zebrafish. Further, our approach allows for extraction of differentially expressed genes in both species, and for quantitative comparison of neural activity at cellular resolution. The web-based and interactive nature of this atlas platform will facilitate the teleost community’s research and its easy extensibility will encourage contributions to its continuous expansion.