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008 131021t2013 my da f abm 000 0 eng d
020 _aTHE0002347(Local)
039 9 _a201905271139
_bshamsul
_c201710121002
_daishah
_y201310211436
_znabilah
040 _aUMP
090 _aSD536 .K36 2013 rs Thesis
100 0 _aNik Kamariah Nik Ismail
245 1 0 _aWood species recognition based on phase - only correlation (POC) technique /
_cNik Kamariah Nik Ismail
260 _aKuantan, Pahang :
_bUMP,
_c2013
300 _axiii, 130 p. :
_bill. ;
_c30 cm. +
_e1 CD-ROM
502 _aThesis (Master of Computer Science) -- Universiti Malaysia Pahang – 2013
504 _aBibliography : p. 101-107
520 3 _aThis 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.
650 0 _aWood
_xIdentification
856 4 0 _uhttp://ecollib.ump.edu.my/25657/
_zLibrary access only
999 _aVIRTUA40
_c4111
_d4117
999 _aVTLSSORT0080*0200*0400*0900*1000*2450*2600*3000*5020*5040*5200*6500*8560*9992