Volume data set visualization using topological zone segmentation.
Item
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Title
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Volume data set visualization using topological zone segmentation.
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Identifier
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AAI3024786
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identifier
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3024786
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Creator
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Ferdous, Nazma.
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Contributor
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Adviser: James L. Cox
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Date
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2001
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Language
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English
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Publisher
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City University of New York.
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Subject
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Computer Science
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Abstract
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For the last two decades, different aspects of visualization have been very popular area of research due to increasing visualization demands of medical and scientific applications in the late twentieth century. A major portion of this research work has been conducted concerning visualization of volume data sets in general, and isosurface extraction in particular. We summarized some of the currently used algorithms in this field, and their efficiency issues in first few chapters. Certainly, the effort of the visualization specialists has come a long way in improving of both quality and applicability. Many more issues remain unresolved, particularly when visualizing large volume data sets, such as the bottleneck induced by excessive I/O operations.;We present an algorithm for organizing the discrete scalar volume data on external storage with important application to out-of-core visualization of extremely large data sets. The application include extraction isosurfaces in a manner that minimizes both I/O and disk seek time, topologically correct isosurface simplification and producing a visual atlas of all topologically distinct objects in the data set, with the range of scalar isovalues that reveal each. The segmentation algorithm computes the region of space called topological zone components, so that any isosurface component is completely contained in a zone component and all contours contained in a zone component are homeomorphic. The algorithm also develops a search structure called criticality tree as by-product and both of these computation is carried out in space efficient manner. The algorithm is very generic in nature. It does not assume any specific structure of the input data or any specific interpolants and can be extended to data sets with non-unique values. Towards the end, we give a simple, efficient and provably correct algorithm for constructing isosurfaces. Finally we present the results obtained by various experiments that justifies our assumption.
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Type
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dissertation
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Source
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PQT Legacy CUNY.xlsx
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degree
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Ph.D.
