Image resizing using thin-plate spline

Interpolation of scattered data refers to the problem of passing a smooth surface through a non-uniform distribution of data samples. In many science and engineering fields, where data are often generated or measured at few and irregular positions, this problem is of practical importance. Over the p...

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Main Author:	Saeedinejad, Behrad
Format:	Thesis
Language:	English
Published:	2014
Subjects:	TK Electrical engineering Electronics Nuclear engineering
Online Access:	http://eprints.utm.my/id/eprint/48849/25/BehradSaeedinejadMFKE2014.pdf
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spelling	my-utm-ep.488492020-07-01T06:24:06Z Image resizing using thin-plate spline 2014-06 Saeedinejad, Behrad TK Electrical engineering. Electronics Nuclear engineering Interpolation of scattered data refers to the problem of passing a smooth surface through a non-uniform distribution of data samples. In many science and engineering fields, where data are often generated or measured at few and irregular positions, this problem is of practical importance. Over the past decades, different methods have been used to yield solutions to the multi-variate scattered data interpolation problem. One of the popular methods that is commonly used is Thin-Plate Spline (TPS). A thin-plate spline is a physically inspired two-dimensional interpolation structure for randomly spaced tabulated data (xi,yi,f(xi,yi)). TPS is the generalization of the natural cubic spline in one dimension. The spline surface represents a thin sheet of metal that is limited not to move at the grid points. Such surfaces are preferred for various modeling and design applications. For decades, TPS had been used in mechanics and engineering, and they were initiated to image analysis community by Bookstein. TPS is practically one of the most frequently used transformation function in non-rigid image registration. In this project, TPS is used for the image resizing purpose and its result shows around 12% improvement in terms of quality compared with Bicubic interpolation method. Furthermore, an approach is proposed to reduce the computational cost drastically for large scale images. The results show that this method speeds up the evaluation of TPS interpolation function up to 16 times, compared with direct evaluation. This approach involves windowing the image in order to implement TPS on smaller data sets rather than applying it to the whole image at once. 2014-06 Thesis http://eprints.utm.my/id/eprint/48849/ http://eprints.utm.my/id/eprint/48849/25/BehradSaeedinejadMFKE2014.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:83670 masters Universiti Teknologi Malaysia, Faculty of Electrical Engineering Faculty of Electrical Engineering
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	TK Electrical engineering Electronics Nuclear engineering
spellingShingle	TK Electrical engineering Electronics Nuclear engineering Saeedinejad, Behrad Image resizing using thin-plate spline
description	Interpolation of scattered data refers to the problem of passing a smooth surface through a non-uniform distribution of data samples. In many science and engineering fields, where data are often generated or measured at few and irregular positions, this problem is of practical importance. Over the past decades, different methods have been used to yield solutions to the multi-variate scattered data interpolation problem. One of the popular methods that is commonly used is Thin-Plate Spline (TPS). A thin-plate spline is a physically inspired two-dimensional interpolation structure for randomly spaced tabulated data (xi,yi,f(xi,yi)). TPS is the generalization of the natural cubic spline in one dimension. The spline surface represents a thin sheet of metal that is limited not to move at the grid points. Such surfaces are preferred for various modeling and design applications. For decades, TPS had been used in mechanics and engineering, and they were initiated to image analysis community by Bookstein. TPS is practically one of the most frequently used transformation function in non-rigid image registration. In this project, TPS is used for the image resizing purpose and its result shows around 12% improvement in terms of quality compared with Bicubic interpolation method. Furthermore, an approach is proposed to reduce the computational cost drastically for large scale images. The results show that this method speeds up the evaluation of TPS interpolation function up to 16 times, compared with direct evaluation. This approach involves windowing the image in order to implement TPS on smaller data sets rather than applying it to the whole image at once.
format	Thesis
qualification_level	Master's degree
author	Saeedinejad, Behrad
author_facet	Saeedinejad, Behrad
author_sort	Saeedinejad, Behrad
title	Image resizing using thin-plate spline
title_short	Image resizing using thin-plate spline
title_full	Image resizing using thin-plate spline
title_fullStr	Image resizing using thin-plate spline
title_full_unstemmed	Image resizing using thin-plate spline
title_sort	image resizing using thin-plate spline
granting_institution	Universiti Teknologi Malaysia, Faculty of Electrical Engineering
granting_department	Faculty of Electrical Engineering
publishDate	2014
url	http://eprints.utm.my/id/eprint/48849/25/BehradSaeedinejadMFKE2014.pdf
_version_	1747817486709948416

Image resizing using thin-plate spline

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