Abstract:

This paper proposes an automatic method of pore combination recognition, which is an important feature to hardwood recognition. After extracting edge from wood microscopic cross-section, based on area histogram of the similar circle regions, the method classifies all regions into two classes with maximum between-class variance, so as to distinguish the pore from other textures, which are similar in shapes but different in sizes. Meanwhile, second objective function about average area of closed regions is used to improve the pore segmentation performance. At last, the method uses adjacency degree of pore set to judge pore combination. The experiments demonstrate that the task of pore segmentation can be completed successfully for all kinds of pore distribution and combination, and also the correct combinations of pores are given.

Key words: adjacency degree of pore set, multi-objective optimization, pore segmentation, pore combination, maximum between-class variance, hard wood