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Gradient Dependent Reconstruction from Scalar Data

Bhattacharya, Arindam
http://rave.ohiolink.edu/etdc/view?acc_num=osu1449181983

Abstract Details

2015, Doctor of Philosophy, Ohio State University, Computer Science and Engineering.
Computed Tomography (CT) is widely accepted as an important tool in medicine. Increasingly, CT is finding a wide variety of application in material science and engineering fields. CT is being used for non-destructive inspection and characterization of aeronautic and automobile components. These components have wide variations in geometry and material characteristics, from single solid piece of metals such as aluminum to exotic composite materials and from micro scale engine parts to large scale airplane tail fins. Unlike organic parts, machine parts often have `sharp' features. Consequently, feature sensitive reconstruction from volume data has seen sporadic but critical work in the recent years. A number of these papers present algorithms to construct isosurfaces with sharp edges and corners from Hermite data, i.e. data containing the exact surface normals at the exact intersection of the surface and grid edges. Such surface normals are not available with CT data. In this thesis, we discuss some fundamental problems with the previous algorithms and the difficulties in using these algorithms on real CT (scalar data) and further describe a new approach to feature reconstruction from volume data. Feature sensitive reconstruction is based on the ability to approximate surface normals from scalar field gradients. Change in gradients can also be used to measure local directions of geometric structures in CT data . We describe a method to extract fiber bundle directions from industrial CT of fiber composites using gradients. Specifically this dissertation proposes, 1) a method to reconstruct isosurfaces from scalar data while preserving sharp features (edges and corners) given a scalar grid and the gradients at the grid locations; 2) a method to select the correct gradients at the grid locations which will be used as input to the above algorithm; 3) Finally, a method for extracting and visualizing fiber bundles in fiber reinforced composites scanned with X-ray computed tomography (XCT).
Wenger Rephael (Advisor)
Shen Han Wei (Committee Member)
Dey Tamal (Committee Member)
183 p.
Computer Science
Isosurface Reconstruction, Surface Reconstruction from Industrial CT data, Fiber Bundle Extraction, Dual Contouring, feature sensitive reconstruction

Recommended Citations

Citations

  • Bhattacharya, A. (2015). Gradient Dependent Reconstruction from Scalar Data [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1449181983

    APA Style (7th edition)

  • Bhattacharya, Arindam. Gradient Dependent Reconstruction from Scalar Data. 2015. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1449181983.

    MLA Style (8th edition)

  • Bhattacharya, Arindam. "Gradient Dependent Reconstruction from Scalar Data." Doctoral dissertation, Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1449181983

    Chicago Manual of Style (17th edition)

osu1449181983
898
© 2015, all rights reserved.
This open access ETD is published by The Ohio State University and OhioLINK.