Abstract
A cascaded wood species recognition system using simple statistical properties of the wood texture is presented where a total of 24 statistical features are extracted from each wood sample. They are mainly vessel features that allow a broad initial grouping of wood texture using fuzzy logic. Then, a neural network classifier is used to refine the broad grouping into the final wood species classification. The proposed system emulates the classification approach normally taken by human experts when analyzing wood species based on texture. A comprehensive set of experiments was performed on a database composed of 3000 macroscopic images of 30 different wood species to evaluate the effectiveness of the system. Finally, its performance is compared with previous works in terms of classification accuracy.
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Acknowledgements
The authors would like to thank Malaysian Ministry of Higher Education (MOHE) and University of Malaya for funding this research through BKP Grant (BK047-204) and UMRG Grant (RP023-2012B). The authors also would like to thank Forest Research Institute of Malaysia (FRIM) for providing us with the wood samples.
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Ibrahim, I., Khairuddin, A.S.M., Arof, H. et al. Statistical feature extraction method for wood species recognition system. Eur. J. Wood Prod. 76, 345–356 (2018). https://doi.org/10.1007/s00107-017-1163-1
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DOI: https://doi.org/10.1007/s00107-017-1163-1