Visualisation interactive de grands volumes de données incertaines : pour une approche perceptive

Alexandre Coninx 1, 2, 3, 4
2 ARTIS - Acquisition, representation and transformations for image synthesis
Inria Grenoble - Rhône-Alpes, INPG - Institut National Polytechnique de Grenoble , LJK - Laboratoire Jean Kuntzmann
3 MAVERICK - Models and Algorithms for Visualization and Rendering
LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble , Inria Grenoble - Rhône-Alpes
Abstract : Current scientific and engineering works make an increasingly frequent use of numerical simulation techniques to study complex physical phenomenons. Visualizing these simulations' results on their geometric structure is often necessary in order to understand and analyze the simulated system. Such a visualization requires specific software tools in order to achieve a comprehensive and accurate depiction of the information present in the dataset. This includes taking into account the available information about dataset quality and data uncertainty. The goal of this thesis is to improve the visualization techniques for scalar data fields in order to integrate uncertainty information to the result. Our work follows a perceptual approach, using knowledge and experimental methods from visual perception research to put forward, study and implement new visualization techniques. A review of the state of the art on uncertainty visualization make us suggest to use an animated procedural noise as a visual primitive to show uncertainty. We set up a psychophysics experiment to evaluate contrast sensitivity thresholds for luminance stimuli generated using Perlin's noise algorithm, and therefore understand under which conditions such noise patterns can be perceived. These results are validated and extended by using a computational model of contrast sensitiviy, which we reimplemented and ran on our stimuli. The resulting information allow us to put forward a new technique for visualizing uncertain scalar data using an animated procedural noise and color maps. The resulting visualization is intuitive and efficient even for datasets with a complex tridimensional geometry. We apply this new technique to two industrial datasets, and demonstrate it to expert users. Their feedback uphold the usabiliy and efficiency of our technique, and allows us to add a few more improvements and to orient our future work.
Document type :
Mathématiques générales [math.GM]. Université de Grenoble, 2012. Français. <NNT : 2012GRENM039>
Contributor : Abes Star <>
Submitted on : Wednesday, January 9, 2013 - 10:55:21 AM
Last modification on : Thursday, September 29, 2016 - 1:22:40 AM
Document(s) archivé(s) le : Wednesday, April 10, 2013 - 3:49:47 AM


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  • HAL Id : tel-00749885, version 2



Alexandre Coninx. Visualisation interactive de grands volumes de données incertaines : pour une approche perceptive. Mathématiques générales [math.GM]. Université de Grenoble, 2012. Français. <NNT : 2012GRENM039>. <tel-00749885v2>




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