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    Author(s): B. H. Bond; D. Earl Kline; Philip A. Araman
    Date: 2002
    Source: Wood and Fiber Science. 34(4): 516-528.
    Publication Series: Miscellaneous Publication
    PDF: View PDF  (104.16 KB)


    Defect color, shape, and density measures aid in the differentiation of knots, bark pockets, stain/mineral streak, and clearwood in red oak, (Quercus rubra). Various color, shape, and density measures were extracted for defects present in color and X-ray images captured using a color line scan camera and an X-ray line scan detector. Analysis of variance was used to determine which color, shape, and density measures differed between defects. Discriminant classifiers were used to test which defect measures best discriminated between different defects in lumber.

    The ANOVA method of model measure selection was unable to provide a direct method of selecting the optimum combination of measures; however, it did provide insight as to which measure should be selected in cases of confusion between defects. No single sensor measure provided overall classification accuracy greater than 70%, indicating the need for multisensor and multimeasure information for defect classification. When used alone, color measures resulted in the highest overall defect classification accuracy (between 69 and 70%). Shape and density measures resulted in the lowest overall classification accuracy (between 32 and 53%); however, when used in combination with other measures, they contributed to a 5–10% increase in defect classification accuracy. It was determined that defect classification required multisensor information to obtain the highest accuracy. For classifying defects in red oak, sensor measures should include two color mean values and two standard deviation values, a shape measure, and a X-ray standard deviation value.

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    Bond, B. H.; Kline, D. Earl; Araman, Philip A. 2002. Differentiating defects in red oak lumber by discriminant analysis using color, shape, and density. Wood and Fiber Science. 34(4): 516-528.


    lumber scanning, defect detection, discriminant analysis, machine vision

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