Wood species recognition based on phase - only correlation (POC) technique

This research was performed in order to determine suitable methodology and techniques to automatically. identify wood species. Traditional wood recognition requires employees who are skilled in recognizing wood species. The two main problems with recognition is the lack of skilled employees and the...

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التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Nik Kamariah, Nik Ismail
التنسيق:	أطروحة
اللغة:	English
منشور في:	2013
الموضوعات:	QA76 Computer software
الوصول للمادة أونلاين:	http://umpir.ump.edu.my/id/eprint/37233/1/Wood%20species%20recognition%20based%20on%20phase%20-%20only%20correlation%20%28POC%29%20technique.pdf
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id	my-ump-ir.37233
record_format	uketd_dc
spelling	my-ump-ir.372332023-03-09T01:43:57Z Wood species recognition based on phase - only correlation (POC) technique 2013-05 Nik Kamariah, Nik Ismail QA76 Computer software This research was performed in order to determine suitable methodology and techniques to automatically. identify wood species. Traditional wood recognition requires employees who are skilled in recognizing wood species. The two main problems with recognition is the lack of skilled employees and the visually impaired employees. This leads to an interruption of the wood recognition process. Besides that, based on the previous research, two techniques are used to recognize wood species. These techniques often do not give a high accuracy of wood species recognition. Accordingly, to improve the accuracy rate of wood species recognition, this research was proposed. There are four main steps performed to develop a wood species recognition system. These are; data acquisition, pre- processing, features extraction, and features analysis. The technique used was the Phase-Only Correlation (POC) technique. The POC technique involves three steps during the feature extraction phase. These steps are the 2D Discrete Fourier Transform (2D DFT), Cross Phase Spectrum, and 2D Inverse Fourier Transform (2D IFT). Each step contains specific calculations and algorithms to obtain details on the wood’s features and match them with the possible matching species. By applying all three steps, the test image is found to either match or not match the training data. Next, the classification process is performed with the help of the K-Neighbour Network (KNN) technique; where it helps to classify the wood images. This technique was used to test eight wood species , namely Balau, Ramin, Durian, Melunak, Mersawa Gajah, Rengas, and Sepetir. From this testing, it was found that Balau and Ramin wood species were identified with 100% accuracy. Meanwhile, Melunak and Rengas wood species were identified to approximately 98.5%; Mersawa to 95%.%; and for both Durian and Sepetir wood species, 93.75% accuracy. was obtained. Furthermore, for the Mersawa Gajah wood species, only 91.25% eaccuracy was obtained. These research results show that 96.4% of the wood species tested accurately and were recognized without error. 2013-05 Thesis http://umpir.ump.edu.my/id/eprint/37233/ http://umpir.ump.edu.my/id/eprint/37233/1/Wood%20species%20recognition%20based%20on%20phase%20-%20only%20correlation%20%28POC%29%20technique.pdf pdf en public masters Universiti Malaysia Pahang Faculty of Computer System And Software Engineering Jasni, Mohamad Zain
institution	Universiti Malaysia Pahang Al-Sultan Abdullah
collection	UMPSA Institutional Repository
language	English
advisor	Jasni, Mohamad Zain
topic	QA76 Computer software
spellingShingle	QA76 Computer software Nik Kamariah, Nik Ismail Wood species recognition based on phase - only correlation (POC) technique
description	This research was performed in order to determine suitable methodology and techniques to automatically. identify wood species. Traditional wood recognition requires employees who are skilled in recognizing wood species. The two main problems with recognition is the lack of skilled employees and the visually impaired employees. This leads to an interruption of the wood recognition process. Besides that, based on the previous research, two techniques are used to recognize wood species. These techniques often do not give a high accuracy of wood species recognition. Accordingly, to improve the accuracy rate of wood species recognition, this research was proposed. There are four main steps performed to develop a wood species recognition system. These are; data acquisition, pre- processing, features extraction, and features analysis. The technique used was the Phase-Only Correlation (POC) technique. The POC technique involves three steps during the feature extraction phase. These steps are the 2D Discrete Fourier Transform (2D DFT), Cross Phase Spectrum, and 2D Inverse Fourier Transform (2D IFT). Each step contains specific calculations and algorithms to obtain details on the wood’s features and match them with the possible matching species. By applying all three steps, the test image is found to either match or not match the training data. Next, the classification process is performed with the help of the K-Neighbour Network (KNN) technique; where it helps to classify the wood images. This technique was used to test eight wood species , namely Balau, Ramin, Durian, Melunak, Mersawa Gajah, Rengas, and Sepetir. From this testing, it was found that Balau and Ramin wood species were identified with 100% accuracy. Meanwhile, Melunak and Rengas wood species were identified to approximately 98.5%; Mersawa to 95%.%; and for both Durian and Sepetir wood species, 93.75% accuracy. was obtained. Furthermore, for the Mersawa Gajah wood species, only 91.25% eaccuracy was obtained. These research results show that 96.4% of the wood species tested accurately and were recognized without error.
format	Thesis
qualification_level	Master's degree
author	Nik Kamariah, Nik Ismail
author_facet	Nik Kamariah, Nik Ismail
author_sort	Nik Kamariah, Nik Ismail
title	Wood species recognition based on phase - only correlation (POC) technique
title_short	Wood species recognition based on phase - only correlation (POC) technique
title_full	Wood species recognition based on phase - only correlation (POC) technique
title_fullStr	Wood species recognition based on phase - only correlation (POC) technique
title_full_unstemmed	Wood species recognition based on phase - only correlation (POC) technique
title_sort	wood species recognition based on phase - only correlation (poc) technique
granting_institution	Universiti Malaysia Pahang
granting_department	Faculty of Computer System And Software Engineering
publishDate	2013
url	http://umpir.ump.edu.my/id/eprint/37233/1/Wood%20species%20recognition%20based%20on%20phase%20-%20only%20correlation%20%28POC%29%20technique.pdf
_version_	1783732267178262528

Wood species recognition based on phase - only correlation (POC) technique

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