Large-Scale Context-Aware Volume Navigation using Dynamic Insets

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Type
Thesis

Authors
Al-Awami, Ali K.

Advisors
Hadwiger, Markus

Committee Members
Knox, Christopher
Wonka, Peter

Program
Computer Science

KAUST Department
Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

Date
2012-07

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.

Citation
Al-Awami, A. K. (2012). Large-Scale Context-Aware Volume Navigation using Dynamic Insets. KAUST Research Repository. https://doi.org/10.25781/KAUST-119US

DOI
10.25781/KAUST-119US

Permanent link to this record
http://hdl.handle.net/10754/244615
Collections
MS Theses
Computer Science Program
Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division