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Large scale feature extraction and tracking
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Large scale feature extraction and tracking
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Dhume
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Pinakin
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Pinakin Dhume
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author
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Silver
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Deborah
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Advisory Committee
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Deborah Silver
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chair
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Wilder
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Joseph
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Joseph Wilder
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internal member
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Marsic
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Ivan
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Advisory Committee
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Ivan Marsic
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Rutgers University
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Graduate School - New Brunswick
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theses
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2007
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2007
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English
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electronic
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xi, 115 pages
Abstract
The goal of Scientific Visualization is to provide a more intuitive interpretation of the data being presented. Traditionally, visualization of 3D time-varying datasets is done using animation based on either iso-surfacing or volume rendering. However, for datasets with continuously evolving features it is difficult to follow and see patterns in 3D. An automated procedure to track features and to detect particular events in their evolution can help scientists to concentrate on regions and phenomena of interest.
Today, computations and simulations are performed on massively parallel computers leading to thousands of datasets where each datasets can be on the order of gigabytes. Visualizing and quantifying such data cannot be done on a single processor machine. Therefore, a distributed form of the feature extraction and feature tracking algorithms is required. In the Vizlab, we have developed a number of tools to extract and track features on a parallel cluster. However, there are cases where one would like to handle large datasets. In this thesis, we extend the feature extraction and tracking library to perform feature extraction on a large datasets with multiple steps, i.e., reading in only a portion of the data at once. In addition to the distributed feature tracking, we also enhanced the visualization component so that features can be accessed and rendered in a more intuitive fashion.
Today, computations and simulations are performed on massively parallel computers leading to thousands of datasets where each datasets can be on the order of gigabytes. Visualizing and quantifying such data cannot be done on a single processor machine. Therefore, a distributed form of the feature extraction and feature tracking algorithms is required. In the Vizlab, we have developed a number of tools to extract and track features on a parallel cluster. However, there are cases where one would like to handle large datasets. In this thesis, we extend the feature extraction and tracking library to perform feature extraction on a large datasets with multiple steps, i.e., reading in only a portion of the data at once. In addition to the distributed feature tracking, we also enhanced the visualization component so that features can be accessed and rendered in a more intuitive fashion.
Note
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M.S.
Note
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Includes bibliographical references (p. 114-115).
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Electrical and Computer Engineering
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Computational grids (Computer systems)
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High performance computing
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Parallel processing (Electronic computers)
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Visualization
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Graduate School - New Brunswick Electronic Theses and Dissertations
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http://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.15832
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doi:10.7282/T3HX1D46
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ETD graduate
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Open
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PINAKIN DHUME
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Rutgers University. Graduate School - New Brunswick
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Non-exclusive ETD license
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I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
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