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Gaussian Process Modeling for Upsamp...

Reeves, Steven I.

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Gaussian Process Modeling for Upsampling Algorithms with Applications in Computer Vision and Computational Fluid Dynamics.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Gaussian Process Modeling for Upsampling Algorithms with Applications in Computer Vision and Computational Fluid Dynamics./
Author:	Reeves, Steven I.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
Description:	145 p.
Notes:	Source: Dissertations Abstracts International, Volume: 81-11, Section: B.
Contained By:	Dissertations Abstracts International81-11B.
Subject:	Artificial intelligence. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27740695
ISBN:	9798641843223

Gaussian Process Modeling for Upsampling Algorithms with Applications in Computer Vision and Computational Fluid Dynamics.
Reeves, Steven I.

Gaussian Process Modeling for Upsampling Algorithms with Applications in Computer Vision and Computational Fluid Dynamics. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 145 p.

Source: Dissertations Abstracts International, Volume: 81-11, Section: B.

Thesis (Ph.D.)--University of California, Santa Cruz, 2020.

This item must not be sold to any third party vendors.

Across a variety of fields, interpolation algorithms have been used to upsample low resolution or coarse data fields. In this work, novel Gaussian Process based methods are employed to solve a variety of upsampling problems. Specifically three applications are explored: coarse data prolongation in Adaptive Mesh Refinement (AMR) in the field of Computational Fluid Dynamics, accurate document image upsampling to enhance Optical Character Recognition (OCR) accuracy, and fast and accurate Single Image Super Resolution (SISR). For AMR, a new, efficient, and "3rd order accurate" algorithm called GP-AMR is presented. Next, a novel, non-zero mean, windowed GP model is generated to upsample low resolution document images to generate a higher OCR accuracy, when compared to the industry standard. Finally, a hybrid GP convolutional neural network algorithm is used to generate a computationally efficient and high quality SISR model.

ISBN: 9798641843223Subjects--Topical Terms:

516317
Artificial intelligence.
Subjects--Index Terms:

Adaptive mesh refinement

Gaussian Process Modeling for Upsampling Algorithms with Applications in Computer Vision and Computational Fluid Dynamics.
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020 $a 9798641843223
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100 1 $a Reeves, Steven I. $3 3547575
245 1 0 $a Gaussian Process Modeling for Upsampling Algorithms with Applications in Computer Vision and Computational Fluid Dynamics.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2020
300 $a 145 p.
500 $a Source: Dissertations Abstracts International, Volume: 81-11, Section: B.
500 $a Advisor: Lee, Dongwook.
502 $a Thesis (Ph.D.)--University of California, Santa Cruz, 2020.
506 $a This item must not be sold to any third party vendors.
520 $a Across a variety of fields, interpolation algorithms have been used to upsample low resolution or coarse data fields. In this work, novel Gaussian Process based methods are employed to solve a variety of upsampling problems. Specifically three applications are explored: coarse data prolongation in Adaptive Mesh Refinement (AMR) in the field of Computational Fluid Dynamics, accurate document image upsampling to enhance Optical Character Recognition (OCR) accuracy, and fast and accurate Single Image Super Resolution (SISR). For AMR, a new, efficient, and "3rd order accurate" algorithm called GP-AMR is presented. Next, a novel, non-zero mean, windowed GP model is generated to upsample low resolution document images to generate a higher OCR accuracy, when compared to the industry standard. Finally, a hybrid GP convolutional neural network algorithm is used to generate a computationally efficient and high quality SISR model.
590 $a School code: 0036.
650 4 $a Artificial intelligence. $3 516317
650 4 $a Applied mathematics. $3 2122814
650 4 $a Computational physics. $3 3343998
653 $a Adaptive mesh refinement
653 $a Computational science
653 $a Deep learning
653 $a Gaussian processes
653 $a Image processing
690 $a 0800
690 $a 0364
690 $a 0216
710 2 $a University of California, Santa Cruz. $b Applied Mathematics and Statistics. $3 3177153
773 0 $t Dissertations Abstracts International $g 81-11B.
790 $a 0036
791 $a Ph.D.
792 $a 2020
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27740695