Title :
Defects detection based on principal component analyses and support vector machines
Author_Institution :
Dept. of Control Sci. & Eng., Coll. of Electron. & Inf. Eng., Shanghai, China
Abstract :
Woods are used in many fields. The appearance of woods is important for the quality of wood products. In this paper, we present an image series fusion method based principal component analyses and recognize the defects by support vector machines. We select the histogram of the feature image as feature vector, and send it to support vector machines for recognition and classification. The results show that this method can fuse the image series and detect the defects.
Keywords :
feature extraction; image fusion; image recognition; principal component analysis; support vector machines; wood; defect recognition; defects detection; feature image histogram; image series fusion method; principal component analyses; support vector machines; wood appearance; wood product quality; Image series Fusion; Principal component analyses; Support vector machines; defects detection;
Conference_Titel :
Computer and Communication Technologies in Agriculture Engineering (CCTAE), 2010 International Conference On
Conference_Location :
Chengdu
Print_ISBN :
978-1-4244-6944-4
DOI :
10.1109/CCTAE.2010.5544326