Abstract:

Based on wavelet method, it realized multi_resolutional spectrum decomposition of wood surface texture, and analyzed frequency traits of wood texture at horizontal, vertical and angular directions by eigenvalues from decomposition subsections, furthermore it compared texture differences of softwood with those of hardwood, and radial section with tangential section. It proposed to use standard deviation of sub-image energies to select an optimal wavelet decomposition scale for wood texture, and found out a better selection of filter length at 8 and a best decomposition scale at 2 for wood. Also in this paper, it indicated that energies of subsection HL and LL can be used as key parameters for texture distinction and classification, and EHL/ELH can be used to declare texture direction of wood.

Key words: wood, texture, wavelet