Statistical properties of lines distribution for tropical wood recognition system

An automated tropical wood recognition system has been developed by Centre for Artificial Intelligence & Robotics (CAIRO), Universiti Teknologi Malaysia (UTM) based on machine vision to emulate the experts known as KenalKayu. The system used Statistical Properties of Pores Distribution (SPPD) fe...

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Main Author:	Abdul Ghapar, Hafizza
Format:	Thesis
Language:	English
Published:	2022
Subjects:	T Technology (General)
Online Access:	http://eprints.utm.my/id/eprint/99608/1/HafizzaAbdulGhaparMMJIIT2022.pdf
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spelling	my-utm-ep.996082023-03-05T08:32:40Z Statistical properties of lines distribution for tropical wood recognition system 2022 Abdul Ghapar, Hafizza T Technology (General) An automated tropical wood recognition system has been developed by Centre for Artificial Intelligence & Robotics (CAIRO), Universiti Teknologi Malaysia (UTM) based on machine vision to emulate the experts known as KenalKayu. The system used Statistical Properties of Pores Distribution (SPPD) feature extractor and K-Nearest Neighbor (KNN) classifier which have been proven to increase the system’s accuracy. Unfortunately, when more wood species were added to the system’s database, it reduces the accuracy of the system. Therefore, providing additional features that are representation of each species is one way to improve this issue. As the wood surface pattern is not only defined by pores but lines as well, this thesis presents additional new feature extraction method based on Statistical Properties of Line Distribution (SPLD) to capture the discriminant line features of each species and K-Nearest Neighbor (KNN) is used as classifier. The results of experiments showed that when used by itself as a feature extractor, the SPLD managed to achieve 88% accuracy, and the accuracy increased to 99.5% when combined with SPPD features and 100% accuracy was achieved when SPPD and Basic Grey Level Aura Matrix (BGLAM) features were used in combination. In conclusion, the SPLD method is an essential customized feature extractor and could be used as an alternative to adequately identify wood species. Hence, in the future, other discriminant features can also be added for wood identification purposes. 2022 Thesis http://eprints.utm.my/id/eprint/99608/ http://eprints.utm.my/id/eprint/99608/1/HafizzaAbdulGhaparMMJIIT2022.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:150860 masters Universiti Teknologi Malaysia Malaysia-Japan International Institute of Technology (MJIIT)
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	T Technology (General)
spellingShingle	T Technology (General) Abdul Ghapar, Hafizza Statistical properties of lines distribution for tropical wood recognition system
description	An automated tropical wood recognition system has been developed by Centre for Artificial Intelligence & Robotics (CAIRO), Universiti Teknologi Malaysia (UTM) based on machine vision to emulate the experts known as KenalKayu. The system used Statistical Properties of Pores Distribution (SPPD) feature extractor and K-Nearest Neighbor (KNN) classifier which have been proven to increase the system’s accuracy. Unfortunately, when more wood species were added to the system’s database, it reduces the accuracy of the system. Therefore, providing additional features that are representation of each species is one way to improve this issue. As the wood surface pattern is not only defined by pores but lines as well, this thesis presents additional new feature extraction method based on Statistical Properties of Line Distribution (SPLD) to capture the discriminant line features of each species and K-Nearest Neighbor (KNN) is used as classifier. The results of experiments showed that when used by itself as a feature extractor, the SPLD managed to achieve 88% accuracy, and the accuracy increased to 99.5% when combined with SPPD features and 100% accuracy was achieved when SPPD and Basic Grey Level Aura Matrix (BGLAM) features were used in combination. In conclusion, the SPLD method is an essential customized feature extractor and could be used as an alternative to adequately identify wood species. Hence, in the future, other discriminant features can also be added for wood identification purposes.
format	Thesis
qualification_level	Master's degree
author	Abdul Ghapar, Hafizza
author_facet	Abdul Ghapar, Hafizza
author_sort	Abdul Ghapar, Hafizza
title	Statistical properties of lines distribution for tropical wood recognition system
title_short	Statistical properties of lines distribution for tropical wood recognition system
title_full	Statistical properties of lines distribution for tropical wood recognition system
title_fullStr	Statistical properties of lines distribution for tropical wood recognition system
title_full_unstemmed	Statistical properties of lines distribution for tropical wood recognition system
title_sort	statistical properties of lines distribution for tropical wood recognition system
granting_institution	Universiti Teknologi Malaysia
granting_department	Malaysia-Japan International Institute of Technology (MJIIT)
publishDate	2022
url	http://eprints.utm.my/id/eprint/99608/1/HafizzaAbdulGhaparMMJIIT2022.pdf
_version_	1776100624629235712

Statistical properties of lines distribution for tropical wood recognition system

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