Abstract
We proposed a detection method for wood defects based on linear discriminant analysis (LDA) and the use of compressed sensor images. Wood surface images were captured, using a camera Oscar F810C IRF camera, and then the image segmentation was performed, and the defect features were extracted from wood board images. To reduce the processing time, LDA algorithm was used to integrate these features and reduce their dimensions. Features after fusion were used to construct a data dictionary and a compressed sensor was designed to recognize the wood defects types. Of the three major defect types, 50 images live knots, dead knots, and cracks were used to test the effects of this method. The average time for feature fusion and classification was 0.446 ms with the classification accuracy of 94%.
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Project funding: This work was supported by the State Forestry Administration “948” projects (2015-4-52), Fundamental Research Funds for the Central Universities (2572016BB05), Natural Science Foundation of Heilongjiang Province (C2015054), and Heilongjiang Postdoctoral Research Fund (LBH-Q14014).
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Corresponding editor: Yu Lei.
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Li, C., Zhang, Y., Tu, W. et al. Soft measurement of wood defects based on LDA feature fusion and compressed sensor images. J. For. Res. 28, 1285–1292 (2017). https://doi.org/10.1007/s11676-017-0395-6
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DOI: https://doi.org/10.1007/s11676-017-0395-6