Image-based Exploration of Large-Scale Pathline Fields

Handle URI:
http://hdl.handle.net/10754/321000
Title:
Image-based Exploration of Large-Scale Pathline Fields
Authors:
Nagoor, Omniah H.
Abstract:
While real-time applications are nowadays routinely used in visualizing large nu- merical simulations and volumes, handling these large-scale datasets requires high-end graphics clusters or supercomputers to process and visualize them. However, not all users have access to powerful clusters. Therefore, it is challenging to come up with a visualization approach that provides insight to large-scale datasets on a single com- puter. Explorable images (EI) is one of the methods that allows users to handle large data on a single workstation. Although it is a view-dependent method, it combines both exploration and modification of visual aspects without re-accessing the original huge data. In this thesis, we propose a novel image-based method that applies the concept of EI in visualizing large flow-field pathlines data. The goal of our work is to provide an optimized image-based method, which scales well with the dataset size. Our approach is based on constructing a per-pixel linked list data structure in which each pixel contains a list of pathlines segments. With this view-dependent method it is possible to filter, color-code and explore large-scale flow data in real-time. In addition, optimization techniques such as early-ray termination and deferred shading are applied, which further improves the performance and scalability of our approach.
Advisors:
Hadwiger, Markus ( 0000-0003-1239-4871 )
Committee Member:
Hadwiger, Markus ( 0000-0003-1239-4871 ) ; Heidrich, Wolfgang ( 0000-0002-4227-8508 ) ; Moshkov, Mikhail ( 0000-0003-0085-9483 )
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Program:
Computer Science
Issue Date:
27-May-2014
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.authorNagoor, Omniah H.en
dc.date.accessioned2014-06-11T22:07:47Z-
dc.date.available2014-06-11T22:07:47Z-
dc.date.issued2014-05-27en
dc.identifier.urihttp://hdl.handle.net/10754/321000en
dc.description.abstractWhile real-time applications are nowadays routinely used in visualizing large nu- merical simulations and volumes, handling these large-scale datasets requires high-end graphics clusters or supercomputers to process and visualize them. However, not all users have access to powerful clusters. Therefore, it is challenging to come up with a visualization approach that provides insight to large-scale datasets on a single com- puter. Explorable images (EI) is one of the methods that allows users to handle large data on a single workstation. Although it is a view-dependent method, it combines both exploration and modification of visual aspects without re-accessing the original huge data. In this thesis, we propose a novel image-based method that applies the concept of EI in visualizing large flow-field pathlines data. The goal of our work is to provide an optimized image-based method, which scales well with the dataset size. Our approach is based on constructing a per-pixel linked list data structure in which each pixel contains a list of pathlines segments. With this view-dependent method it is possible to filter, color-code and explore large-scale flow data in real-time. In addition, optimization techniques such as early-ray termination and deferred shading are applied, which further improves the performance and scalability of our approach.en
dc.language.isoenen
dc.subjectimage-baseden
dc.subjectper-pixel linked listen
dc.subjectpathlines fieldsen
dc.subjectexplorable imagesen
dc.subjectdeferred shadingen
dc.subjectearly-ray terminationen
dc.titleImage-based Exploration of Large-Scale Pathline Fieldsen
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.committeememberHadwiger, Markusen
dc.contributor.committeememberHeidrich, Wolfgangen
dc.contributor.committeememberMoshkov, Mikhailen
thesis.degree.disciplineComputer Scienceen
thesis.degree.nameMaster of Scienceen
dc.person.id123923en
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