Iban Plaited Mat Motif Classification using Adaptive Smoothing

Iban plaited mats hold a significant cultural importance amongst Borneo’s indigenous communities. These mats are intricately crafted with culturally significant patterns, incorporating a mix of diagonal, symmetrical, geometric, and non-geometric designs. Classifying these motifs poses a challenge du...

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Main Author:	Silvia, Joseph
Format:	Thesis
Language:	English
Published:	2024
Subjects:	QA75 Electronic computers Computer science
Online Access:	http://ir.unimas.my/id/eprint/45964/1/Iban%20Plaited%20Mat%20Motif%20Classification%20Using%20Adaptive%20Smoothing_PhD%20THESIS%20FINALREVIEW%20SilviaJoseph_sign.pdf
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spelling	my-unimas-ir.459642024-09-09T07:29:38Z Iban Plaited Mat Motif Classification using Adaptive Smoothing 2024-09-05 Silvia, Joseph QA75 Electronic computers. Computer science Iban plaited mats hold a significant cultural importance amongst Borneo’s indigenous communities. These mats are intricately crafted with culturally significant patterns, incorporating a mix of diagonal, symmetrical, geometric, and non-geometric designs. Classifying these motifs poses a challenge due to their complex stylization and incorporation of multiple smaller patterns surrounding the main motif. This work introduces a new image dataset comprising of cleaned, cropped, and perspective corrected Iban plaited mat motif images. First, the accuracy of the Scale Invariant Feature Transform (SIFT) combined with the Random Sample Consensus (RANSAC) algorithm was assessed on the dataset. The optimal peak threshold value for SIFT is reported to be 2.0e-2, and it achieved 100.0% matching accuracy for scale and rotation set. Next, additional local feature descriptors, combined with RANSAC, were evaluated on an expanded dataset. BRISK+RANSAC emerged as the top performer, achieving an average matching accuracy of 85.4%. BRISK+RANSAC encountered challenges in handling unresolved classes in sets with blur, illumination, and viewpoint changes. This is attributed to the spurious classification of smaller motifs affecting the label assignment. To address this issue, an improved classification method with adaptive smoothing is proposed. This method utilizes dynamic thresholds for edge detection and uses morphological operations to enhance the smoothed edges. The proposed method attained 100.0% accuracy for several sets and obtained an average increase of 10.2% accuracy over the baseline method. UNIMAS 2024-09 Thesis http://ir.unimas.my/id/eprint/45964/ http://ir.unimas.my/id/eprint/45964/1/Iban%20Plaited%20Mat%20Motif%20Classification%20Using%20Adaptive%20Smoothing_PhD%20THESIS%20FINALREVIEW%20SilviaJoseph_sign.pdf text en public phd doctoral UNIMAS Faculty of Computer Science and Information Technology
institution	Universiti Malaysia Sarawak
collection	UNIMAS Institutional Repository
language	English
topic	QA75 Electronic computers Computer science
spellingShingle	QA75 Electronic computers Computer science Silvia, Joseph Iban Plaited Mat Motif Classification using Adaptive Smoothing
description	Iban plaited mats hold a significant cultural importance amongst Borneo’s indigenous communities. These mats are intricately crafted with culturally significant patterns, incorporating a mix of diagonal, symmetrical, geometric, and non-geometric designs. Classifying these motifs poses a challenge due to their complex stylization and incorporation of multiple smaller patterns surrounding the main motif. This work introduces a new image dataset comprising of cleaned, cropped, and perspective corrected Iban plaited mat motif images. First, the accuracy of the Scale Invariant Feature Transform (SIFT) combined with the Random Sample Consensus (RANSAC) algorithm was assessed on the dataset. The optimal peak threshold value for SIFT is reported to be 2.0e-2, and it achieved 100.0% matching accuracy for scale and rotation set. Next, additional local feature descriptors, combined with RANSAC, were evaluated on an expanded dataset. BRISK+RANSAC emerged as the top performer, achieving an average matching accuracy of 85.4%. BRISK+RANSAC encountered challenges in handling unresolved classes in sets with blur, illumination, and viewpoint changes. This is attributed to the spurious classification of smaller motifs affecting the label assignment. To address this issue, an improved classification method with adaptive smoothing is proposed. This method utilizes dynamic thresholds for edge detection and uses morphological operations to enhance the smoothed edges. The proposed method attained 100.0% accuracy for several sets and obtained an average increase of 10.2% accuracy over the baseline method.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Silvia, Joseph
author_facet	Silvia, Joseph
author_sort	Silvia, Joseph
title	Iban Plaited Mat Motif Classification using Adaptive Smoothing
title_short	Iban Plaited Mat Motif Classification using Adaptive Smoothing
title_full	Iban Plaited Mat Motif Classification using Adaptive Smoothing
title_fullStr	Iban Plaited Mat Motif Classification using Adaptive Smoothing
title_full_unstemmed	Iban Plaited Mat Motif Classification using Adaptive Smoothing
title_sort	iban plaited mat motif classification using adaptive smoothing
granting_institution	UNIMAS
granting_department	Faculty of Computer Science and Information Technology
publishDate	2024
url	http://ir.unimas.my/id/eprint/45964/1/Iban%20Plaited%20Mat%20Motif%20Classification%20Using%20Adaptive%20Smoothing_PhD%20THESIS%20FINALREVIEW%20SilviaJoseph_sign.pdf
_version_	1811771582982389760

Iban Plaited Mat Motif Classification using Adaptive Smoothing

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