Image-based Exploration of Iso-surfaces for Large Multi- Variable Datasets using Parameter Space.

Handle URI:
http://hdl.handle.net/10754/292460
Title:
Image-based Exploration of Iso-surfaces for Large Multi- Variable Datasets using Parameter Space.
Authors:
Binyahib, Roba S.
Abstract:
With an increase in processing power, more complex simulations have resulted in larger data size, with higher resolution and more variables. Many techniques have been developed to help the user to visualize and analyze data from such simulations. However, dealing with a large amount of multivariate data is challenging, time- consuming and often requires high-end clusters. Consequently, novel visualization techniques are needed to explore such data. Many users would like to visually explore their data and change certain visual aspects without the need to use special clusters or having to load a large amount of data. This is the idea behind explorable images (EI). Explorable images are a novel approach that provides limited interactive visualization without the need to re-render from the original data [40]. In this work, the concept of EI has been used to create a workflow that deals with explorable iso-surfaces for scalar fields in a multivariate, time-varying dataset. As a pre-processing step, a set of iso-values for each scalar field is inferred and extracted from a user-assisted sampling technique in time-parameter space. These iso-values are then used to generate iso- surfaces that are then pre-rendered (from a fixed viewpoint) along with additional buffers (i.e. normals, depth, values of other fields, etc.) to provide a compressed representation of iso-surfaces in the dataset. We present a tool that at run-time allows the user to interactively browse and calculate a combination of iso-surfaces superimposed on each other. The result is the same as calculating multiple iso- surfaces from the original data but without the memory and processing overhead. Our tool also allows the user to change the (scalar) values superimposed on each of the surfaces, modify their color map, and interactively re-light the surfaces. We demonstrate the effectiveness of our approach over a multi-terabyte combustion dataset. We also illustrate the efficiency and accuracy of our technique by comparing our results with those from a more traditional visualization pipeline.
Advisors:
Hadwiger, Markus ( 0000-0003-1239-4871 )
Committee Member:
Bisetti, Fabrizio ( 0000-0001-5162-7805 ) ; Moshkov, Mikhail ( 0000-0003-0085-9483 ) ; Srinivasan, Madhu
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Program:
Computer Science
Issue Date:
13-May-2013
Type:
Thesis
Appears in Collections:
Theses; Computer Science Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.advisorHadwiger, Markusen
dc.contributor.authorBinyahib, Roba S.en
dc.date.accessioned2013-05-21T08:24:20Z-
dc.date.available2013-05-21T08:24:20Z-
dc.date.issued2013-05-13en
dc.identifier.urihttp://hdl.handle.net/10754/292460en
dc.description.abstractWith an increase in processing power, more complex simulations have resulted in larger data size, with higher resolution and more variables. Many techniques have been developed to help the user to visualize and analyze data from such simulations. However, dealing with a large amount of multivariate data is challenging, time- consuming and often requires high-end clusters. Consequently, novel visualization techniques are needed to explore such data. Many users would like to visually explore their data and change certain visual aspects without the need to use special clusters or having to load a large amount of data. This is the idea behind explorable images (EI). Explorable images are a novel approach that provides limited interactive visualization without the need to re-render from the original data [40]. In this work, the concept of EI has been used to create a workflow that deals with explorable iso-surfaces for scalar fields in a multivariate, time-varying dataset. As a pre-processing step, a set of iso-values for each scalar field is inferred and extracted from a user-assisted sampling technique in time-parameter space. These iso-values are then used to generate iso- surfaces that are then pre-rendered (from a fixed viewpoint) along with additional buffers (i.e. normals, depth, values of other fields, etc.) to provide a compressed representation of iso-surfaces in the dataset. We present a tool that at run-time allows the user to interactively browse and calculate a combination of iso-surfaces superimposed on each other. The result is the same as calculating multiple iso- surfaces from the original data but without the memory and processing overhead. Our tool also allows the user to change the (scalar) values superimposed on each of the surfaces, modify their color map, and interactively re-light the surfaces. We demonstrate the effectiveness of our approach over a multi-terabyte combustion dataset. We also illustrate the efficiency and accuracy of our technique by comparing our results with those from a more traditional visualization pipeline.en
dc.language.isoenen
dc.subjectexplorable imagesen
dc.subjectiso-surfacesen
dc.subjectdeep imagesen
dc.subjectremote visualizationen
dc.subjectdeferred shadingen
dc.subjectimage-based renderingen
dc.titleImage-based Exploration of Iso-surfaces for Large Multi- Variable Datasets using Parameter Space.en
dc.typeThesisen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
thesis.degree.grantorKing Abdullah University of Science and Technologyen_GB
dc.contributor.committeememberBisetti, Fabrizioen
dc.contributor.committeememberMoshkov, Mikhailen
dc.contributor.committeememberSrinivasan, Madhuen
thesis.degree.disciplineComputer Scienceen
thesis.degree.nameMaster of Scienceen
dc.person.id118413en
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