Fig. 1. Typical defective samples

Fig. 2. Flow chart of recognition algorithm

Fig. 3. Illustration of image block

Fig. 4. Original images. (a) Original image of defect of rotary veneer; (b) original image of defect of rotary veneer with texture

Fig. 5. Grayscale images. (a) Grayscale image of rotary veneer; (b) grayscale of rotary veneer with texture

Fig. 6. Grayscale histograms of original images. (a) Grayscale histogram of rotary veneer; (b) grayscale histogram of rotary veneer with texture

Fig. 7. Comparison of gray histograms of normal block and knot block. (a) Gray histogram of normal block of rotary veneer; (b) gray histogram of knot block of rotary veneer; (c) gray histogram of normal block of rotary veneer with texture; (d) gray histogram of knot block of rotary veneer with texture

Fig. 8. Preliminary identification results. (a) Preliminary identification result of rotary veneer; (b) preliminary identification result of rotary veneer with texture

Fig. 9. Parameter optimization result

Fig. 10. Final recognition results. (a) Rotary veneer; (b) rotary veneer with texture

Table 1. Four combinations of true and false predictions

Table 2. Statistics data of rotary veneers%

Table 3. Statistics data of rotary veneers with texture%