Performance study of parallel implementation of texture images using GLCM

The process of the creation of texture images derived from a windowed GLCM coupled with the calculation of Haralick features for each window is a time intensive process due to the intense number of calculations involved. This study examines and seeks to quantify the expected increase in processing s...

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Main Author:	Esa, Salamiah
Format:	Thesis
Language:	English
Published:	2013
Subjects:	TK Electrical engineering Electronics Nuclear engineering
Online Access:	http://eprints.utm.my/id/eprint/43941/5/SalmiahEsaMFKE2013.pdf
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id	my-utm-ep.43941
record_format	uketd_dc
spelling	my-utm-ep.439412017-07-13T04:14:07Z Performance study of parallel implementation of texture images using GLCM 2013-06 Esa, Salamiah TK Electrical engineering. Electronics Nuclear engineering The process of the creation of texture images derived from a windowed GLCM coupled with the calculation of Haralick features for each window is a time intensive process due to the intense number of calculations involved. This study examines and seeks to quantify the expected increase in processing speed when migrating this algorithm from a traditional serial implementation to a parallel implementation of the same function using modern Graphical Processing Units and the effects of certain parameters such as window size and image size. The components of texture images and some of the factors relating to the efficiency of CUDA code are described. The problem domain was analysed and a serial version of texture window analysis was implemented and checked for accuracy by comparing it to code written in Matlab. The serial code was tested on a 2.4 Ghz Intel core i5 processor while the parallel code was tested on two different GPU cards, a GeForce 310M and a GeForce GTX 620. The final (fastest) implementation used three kernels. Two of these performed gray scale conversion and intensity scaling while the third performed the entire GLCM construction and feature extraction. The results showed that a single large kernel could outperform the collection of small kernels that was used in the alternative implementation. As a result of parallel implementation, texture analysis of a 2048 x 2048 pixels image was found to be up to 44 times faster than the serial version using the GeForce 310M and even faster on the GeForce GTX 620. 2013-06 Thesis http://eprints.utm.my/id/eprint/43941/ http://eprints.utm.my/id/eprint/43941/5/SalmiahEsaMFKE2013.pdf application/pdf en public 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 Esa, Salamiah Performance study of parallel implementation of texture images using GLCM
description	The process of the creation of texture images derived from a windowed GLCM coupled with the calculation of Haralick features for each window is a time intensive process due to the intense number of calculations involved. This study examines and seeks to quantify the expected increase in processing speed when migrating this algorithm from a traditional serial implementation to a parallel implementation of the same function using modern Graphical Processing Units and the effects of certain parameters such as window size and image size. The components of texture images and some of the factors relating to the efficiency of CUDA code are described. The problem domain was analysed and a serial version of texture window analysis was implemented and checked for accuracy by comparing it to code written in Matlab. The serial code was tested on a 2.4 Ghz Intel core i5 processor while the parallel code was tested on two different GPU cards, a GeForce 310M and a GeForce GTX 620. The final (fastest) implementation used three kernels. Two of these performed gray scale conversion and intensity scaling while the third performed the entire GLCM construction and feature extraction. The results showed that a single large kernel could outperform the collection of small kernels that was used in the alternative implementation. As a result of parallel implementation, texture analysis of a 2048 x 2048 pixels image was found to be up to 44 times faster than the serial version using the GeForce 310M and even faster on the GeForce GTX 620.
format	Thesis
qualification_level	Master's degree
author	Esa, Salamiah
author_facet	Esa, Salamiah
author_sort	Esa, Salamiah
title	Performance study of parallel implementation of texture images using GLCM
title_short	Performance study of parallel implementation of texture images using GLCM
title_full	Performance study of parallel implementation of texture images using GLCM
title_fullStr	Performance study of parallel implementation of texture images using GLCM
title_full_unstemmed	Performance study of parallel implementation of texture images using GLCM
title_sort	performance study of parallel implementation of texture images using glcm
granting_institution	Universiti Teknologi Malaysia, Faculty of Electrical Engineering
granting_department	Faculty of Electrical Engineering
publishDate	2013
url	http://eprints.utm.my/id/eprint/43941/5/SalmiahEsaMFKE2013.pdf
_version_	1747817122694692864

Performance study of parallel implementation of texture images using GLCM

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