  Surface quality is an extremely important issue for wood products in the
market. Although quality inspection can be made by a human expert while
manufacturing, this operation is prone to errors. One possible solution may be
using standard machine vision techniques to automatically detect defects on
wood surfaces. Due to the random texture on wood surfaces, this solution is
also not possible most of the times. Therefore, more advanced and novel machine
vision techniques are needed to automatically inspect wood surfaces. In this
study, we propose such a solution based on support region extraction from the
gradient magnitude and the Laplacian of Gaussian response of the wood surface
image. We introduce novel structural and conditional statistical features using
these support regions. Then, we classify different defect types on wood
surfaces using our novel features. We tested our automated wood surface
inspection system on a large data set and obtained very promising results.
