Robust wood species recognition using variable color information

In this paper, we propose a robust wood species classification scheme using color wood surface images. First, a novel 2D wood image measurement machine is devised, and the wood RGB color image is converted into a combined gray scale image. Second, the corresponding gray histogram is established, which will be used as the classification feature. Third, an improved snake model is used to fulfill the curve deformation of the histogram curve of the standard specimen. This snake will then converge to the histogram curve of the test specimen. Finally, pattern recognition is performed by comparing the initial and final snakes with the histogram curve of the test specimen. We have experimentally proved that this scheme improves the recognition accuracy, which can efficiently discriminate the intraspecific color variation and the interspecific color variation.