scholarly journals Uncertainty visualization in HARDI based on ensembles of ODFs

Author(s):  
Fangxiang Jiao ◽  
Jeff M. Phillips ◽  
Yaniv Gur ◽  
Chris R. Johnson
2015 ◽  
Vol 8 (7) ◽  
pp. 2355-2377 ◽  
Author(s):  
M. Rautenhaus ◽  
C. M. Grams ◽  
A. Schäfler ◽  
R. Westermann

Abstract. We present the application of interactive three-dimensional (3-D) visualization of ensemble weather predictions to forecasting warm conveyor belt situations during aircraft-based atmospheric research campaigns. Motivated by forecast requirements of the T-NAWDEX-Falcon 2012 (THORPEX – North Atlantic Waveguide and Downstream Impact Experiment) campaign, a method to predict 3-D probabilities of the spatial occurrence of warm conveyor belts (WCBs) has been developed. Probabilities are derived from Lagrangian particle trajectories computed on the forecast wind fields of the European Centre for Medium Range Weather Forecasts (ECMWF) ensemble prediction system. Integration of the method into the 3-D ensemble visualization tool Met.3D, introduced in the first part of this study, facilitates interactive visualization of WCB features and derived probabilities in the context of the ECMWF ensemble forecast. We investigate the sensitivity of the method with respect to trajectory seeding and grid spacing of the forecast wind field. Furthermore, we propose a visual analysis method to quantitatively analyse the contribution of ensemble members to a probability region and, thus, to assist the forecaster in interpreting the obtained probabilities. A case study, revisiting a forecast case from T-NAWDEX-Falcon, illustrates the practical application of Met.3D and demonstrates the use of 3-D and uncertainty visualization for weather forecasting and for planning flight routes in the medium forecast range (3 to 7 days before take-off).


2018 ◽  
Vol 10 (5) ◽  
Author(s):  
Almoctar Hassoumi ◽  
Vsevolod Peysakhovich ◽  
Christophe Hurter

      In this paper, we investigate how visualization assets can support the qualitative evaluation of gaze estimation uncertainty. Although eye tracking data are commonly available, little has been done to visually investigate the uncertainty of recorded gaze information. This paper tries to fill this gap by using innovative uncertainty computation and visualization. Given a gaze processing pipeline, we estimate the location of this gaze position in the world camera. To do so we developed our own gaze data processing which give us access to every stage of the data transformation and thus the uncertainty computation. To validate our gaze estimation pipeline, we designed an experiment with 12 participants and showed that the correction methods we proposed reduced the Mean Angular Error by about 1.32 cm, aggregating all 12 participants’ results. The Mean Angular Error is 0.25° (SD=0.15°) after correction of the estimated gaze. Next, to support the qualitative assessment of this data, we provide a map which codes the actual uncertainty in the user point of view. 


Big Data ◽  
2016 ◽  
pp. 261-287
Author(s):  
Keqin Wu ◽  
Song Zhang

While uncertainty in scientific data attracts an increasing research interest in the visualization community, two critical issues remain insufficiently studied: (1) visualizing the impact of the uncertainty of a data set on its features and (2) interactively exploring 3D or large 2D data sets with uncertainties. In this chapter, a suite of feature-based techniques is developed to address these issues. First, an interactive visualization tool for exploring scalar data with data-level, contour-level, and topology-level uncertainties is developed. Second, a framework of visualizing feature-level uncertainty is proposed to study the uncertain feature deviations in both scalar and vector data sets. With quantified representation and interactive capability, the proposed feature-based visualizations provide new insights into the uncertainties of both data and their features which otherwise would remain unknown with the visualization of only data uncertainties.


2012 ◽  
Vol 29 (4) ◽  
pp. 297-309 ◽  
Author(s):  
R. Brecheisen ◽  
B. Platel ◽  
B. M. ter Haar Romeny ◽  
A. Vilanova

2014 ◽  
Vol 14 (04) ◽  
pp. 1450017
Author(s):  
Ji Ma ◽  
David Murphy ◽  
Gregory Provan ◽  
Cian O'Mathuna ◽  
Michael Hayes

Many techniques have been proposed to represent uncertainty in data visualization. However, little research has been reported on the evaluation of their effectiveness. Moreover, no studies have been conducted to evaluate direct volume rendering (DVR)-based uncertainty visualization techniques. In this paper, we present a novel method that evaluates the perceptual effectiveness of four existing and one proposed DVR-based uncertainty visualization techniques. Four types of searching tasks that include identifying the maximum uncertainty data, identifying the minimum uncertainty data, identifying the maximum scalar data and identifying the minimum scalar data have been involved in this study, and a total of twenty-eight participants have contributed to the final main user study. Our analysis suggested that the proposed linked views and interactive specification (LVIS) technique appears to be the most accurate among all techniques, although it takes the longest task completion time. For the four existing techniques, the overlays technique appears to be the most advantageous, and it takes similar task completion time as the others. We believe that these findings can provide useful guidance for future uncertainty visualization design.


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