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dc.contributor.advisorHadwiger, Markus
dc.contributor.authorAl-Awami, Ali K.
dc.date.accessioned2012-09-18T09:14:45Z
dc.date.available2012-09-18T09:14:45Z
dc.date.issued2012-07
dc.identifier.citationAl-Awami, A. K. (2012). Large-Scale Context-Aware Volume Navigation using Dynamic Insets. KAUST Research Repository. https://doi.org/10.25781/KAUST-119US
dc.identifier.doi10.25781/KAUST-119US
dc.identifier.urihttp://hdl.handle.net/10754/244615
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.
dc.language.isoen
dc.titleLarge-Scale Context-Aware Volume Navigation using Dynamic Insets
dc.typeThesis
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
thesis.degree.grantorKing Abdullah University of Science and Technology
dc.contributor.committeememberKnox, Christopher
dc.contributor.committeememberWonka, Peter
thesis.degree.disciplineComputer Science
thesis.degree.nameMaster of Science


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