Seleksi Arah Sudut Komputasi Dan Fitur Glcm Pada Kstraksi Citra Kayu Jati, Mahoni, Mindi, Dan Sengon
DOI:
https://doi.org/10.32497/wahanats.v23i2.1363Abstract
Research on feature extraction of wood texture with features and angle direction is
rarely done, especially in teak, mahogany, mindi, and albasia. This research is
needed to select more efficient and effective features and angle directions to identify wood species. The features tested were Angular Second Moment (ASM), Contrast, IDM / Homogenity, Entropy, Correlation and the direction of the computational 0, 45, 90, and 135 degrees of gray level co-occurrence matrix (GLCM). The experimental results show that the selected angles are 0, 45, and 90 degrees and features are IDM and Entropy.
Kata kunci : gray level co-occurrence matrix (GLCM), features extraction, wood
classification
References
Bremananth, R., Nithya B, and Saipriya R., 2009, Wood-Species- Recognition-System. World Academy of Science,
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Hu, S., Li, K., Bao, X., 2015, Wood species recognition based on SIFT keypoint histogram. 2015 8th International Congress on Image and Signal Processing (CISP), 2015, Pages: 702 ”“ 706.
Ibrahim, I., Khairuddin, A.S.M., Talip, M.S.A., Yusof, R., 2017, Tree species recognition system based on macroscopic image analysis. Wood Sci Technol, (2017) 51:431”“444, DOI 10.1007/ s00226-016-0859-4.
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