Abstract: There are sometimes defects such as cracks, Bump, etc on the wood surfaces in the production process of wood and furniture. The different texture of the wood surfaces and the reflex light of the varnished surfaces enhance the difficult of defects detection on Wood Surfaces. In order to inspect the defects,we illuminated the surface of wood from four different angels and gained the respective four images for more detail information. A fusion method for the image series based on principal component analysis(PCA) was presented in this paper,which fuses the complementary information of the image series including the four images and gets a result with more distinct defects. This method introduces a principal component subspace and reserves original information when extracting mainly information. The emulated resups show that more distinct features can be extracted from the four images of a same surface by fusing the image series with PCA. I}he extracted features can be used to automatically detect and classify the defects on the wood surfaces in the future task.

Key words: Defects on wood surfaces, Image fusion, Principal component analysis, Image series fusion,Feature extraction