Simulation of greyscale image colouring using blob detection

Automatic colouring of greyscale images using computer is one of the important fields in digital image processing. It helps to produce more appealing visuals to human eye when one have to deal with medical images, night vision cameras or scientific illustrations. However, to produce images that are...

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Main Author:	Azimi, Ahmad Izul Fakhruddin
Format:	Thesis
Language:	English
Published:	2017
Subjects:	QA Mathematics
Online Access:	http://eprints.utm.my/id/eprint/77813/1/AhmadIzulFakhruddinMFS2017.pdf
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id	my-utm-ep.77813
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spelling	my-utm-ep.778132018-07-04T11:47:59Z Simulation of greyscale image colouring using blob detection 2017-01 Azimi, Ahmad Izul Fakhruddin QA Mathematics Automatic colouring of greyscale images using computer is one of the important fields in digital image processing. It helps to produce more appealing visuals to human eye when one have to deal with medical images, night vision cameras or scientific illustrations. However, to produce images that are at par with the ability of human eyes, computerised colouring process takes a lot of time and ample calculation. Recent years, blob detection has shown a good development for finding features in an image. This method not only can run on low memory devices but also provides users with faster calculation. Encouraged by these advantages – work on low memory devices and enable faster calculation, two models of untrained colouring of greyscale images are proposed in this study. The maximum number of blob features is examined using Centre Surround Extremas (CenSurE) and Binary Robust Independent Elementary Features (BRIEF). The result of this study proves that the images coloured by these models look better with increment features of the key point if the minimum matching distance is as low as possible. In addition, when comparing feature descriptors using Fast Retina Keypoint (FREAK) solely and FREAK together with Speeded-Up Robust Features (SURF), it is concluded that the result is getting better with the decrement of minimum Hessian in the image. This experiment leads to the discovery that the selection of feature descriptors will influence the result of colouring. 2017-01 Thesis http://eprints.utm.my/id/eprint/77813/ http://eprints.utm.my/id/eprint/77813/1/AhmadIzulFakhruddinMFS2017.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:105177 masters Universiti Teknologi Malaysia, Faculty of Science Faculty of Science
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QA Mathematics
spellingShingle	QA Mathematics Azimi, Ahmad Izul Fakhruddin Simulation of greyscale image colouring using blob detection
description	Automatic colouring of greyscale images using computer is one of the important fields in digital image processing. It helps to produce more appealing visuals to human eye when one have to deal with medical images, night vision cameras or scientific illustrations. However, to produce images that are at par with the ability of human eyes, computerised colouring process takes a lot of time and ample calculation. Recent years, blob detection has shown a good development for finding features in an image. This method not only can run on low memory devices but also provides users with faster calculation. Encouraged by these advantages – work on low memory devices and enable faster calculation, two models of untrained colouring of greyscale images are proposed in this study. The maximum number of blob features is examined using Centre Surround Extremas (CenSurE) and Binary Robust Independent Elementary Features (BRIEF). The result of this study proves that the images coloured by these models look better with increment features of the key point if the minimum matching distance is as low as possible. In addition, when comparing feature descriptors using Fast Retina Keypoint (FREAK) solely and FREAK together with Speeded-Up Robust Features (SURF), it is concluded that the result is getting better with the decrement of minimum Hessian in the image. This experiment leads to the discovery that the selection of feature descriptors will influence the result of colouring.
format	Thesis
qualification_level	Master's degree
author	Azimi, Ahmad Izul Fakhruddin
author_facet	Azimi, Ahmad Izul Fakhruddin
author_sort	Azimi, Ahmad Izul Fakhruddin
title	Simulation of greyscale image colouring using blob detection
title_short	Simulation of greyscale image colouring using blob detection
title_full	Simulation of greyscale image colouring using blob detection
title_fullStr	Simulation of greyscale image colouring using blob detection
title_full_unstemmed	Simulation of greyscale image colouring using blob detection
title_sort	simulation of greyscale image colouring using blob detection
granting_institution	Universiti Teknologi Malaysia, Faculty of Science
granting_department	Faculty of Science
publishDate	2017
url	http://eprints.utm.my/id/eprint/77813/1/AhmadIzulFakhruddinMFS2017.pdf
_version_	1747817837269876736

Simulation of greyscale image colouring using blob detection

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