Large-Scale Context-Aware Volume Navigation using Dynamic Insets

Handle URI:
http://hdl.handle.net/10754/244615
Title:
Large-Scale Context-Aware Volume Navigation using Dynamic Insets
Authors:
Al-Awami, Ali
Abstract:
Latest developments in electron microscopy (EM) technology produce high resolution images that enable neuro-scientists to identify and put together the complex neural connections in a nervous system. However, because of the massive size and underlying complexity of this kind of data, processing, navigation and analysis suffer drastically in terms of time and effort. In this work, we propose the use of state-of- the-art navigation techniques, such as dynamic insets, built on a peta-scale volume visualization framework to provide focus and context-awareness to help neuro-scientists in their mission to analyze, reconstruct, navigate and explore EM neuroscience data.
Advisors:
Hadwiger, Markus ( 0000-0003-1239-4871 )
Committee Member:
Knox, Christopher; Wonka, Peter ( 0000-0003-0627-9746 )
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Program:
Computer Science
Issue Date:
Jul-2012
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.authorAl-Awami, Alien
dc.date.accessioned2012-09-18T09:14:45Z-
dc.date.available2012-09-18T09:14:45Z-
dc.date.issued2012-07en
dc.identifier.urihttp://hdl.handle.net/10754/244615en
dc.description.abstractLatest developments in electron microscopy (EM) technology produce high resolution images that enable neuro-scientists to identify and put together the complex neural connections in a nervous system. However, because of the massive size and underlying complexity of this kind of data, processing, navigation and analysis suffer drastically in terms of time and effort. In this work, we propose the use of state-of- the-art navigation techniques, such as dynamic insets, built on a peta-scale volume visualization framework to provide focus and context-awareness to help neuro-scientists in their mission to analyze, reconstruct, navigate and explore EM neuroscience data.en
dc.language.isoenen
dc.titleLarge-Scale Context-Aware Volume Navigation using Dynamic Insetsen
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.committeememberKnox, Christopheren
dc.contributor.committeememberWonka, Peteren
thesis.degree.disciplineComputer Scienceen
thesis.degree.nameMaster of Scienceen
dc.person.id113655en
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