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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
http://eprints.utm.my/id/eprint/99608/
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spelling my.utm.996082023-03-05T08:32:40Z http://eprints.utm.my/id/eprint/99608/ Statistical properties of lines distribution for tropical wood recognition system 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 NonPeerReviewed application/pdf en http://eprints.utm.my/id/eprint/99608/1/HafizzaAbdulGhaparMMJIIT2022.pdf Abdul Ghapar, Hafizza (2022) Statistical properties of lines distribution for tropical wood recognition system. Masters thesis, Universiti Teknologi Malaysia. http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:150860
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
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
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
publishDate 2022
url http://eprints.utm.my/id/eprint/99608/1/HafizzaAbdulGhaparMMJIIT2022.pdf
http://eprints.utm.my/id/eprint/99608/
http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:150860
_version_ 1759689426489835520
score 13.211869

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