A new wood recognition method based on gabor entropy

Authors:
Hang-jun Wang;Xiao-Feng Wang
Affiliations:
Department of Automation, University of Science and Technology of China, Hefei, China;Hefei Institute of Intelligent Machines, Chinese Academy of Science, Hefei, China
Venue:
ICIC'11 Proceedings of the 7th international conference on Advanced Intelligent Computing Theories and Applications: with aspects of artificial intelligence
Year:
2011

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Abstract

Correct wood recognition has an important meaning in rational use of wood resources. Automatic wood recognition based on wood stereogram are studied in this paper. According to the wood stereogram characteristics, a method of image normalization is presented firstly. Then wood texture features are extracted using Gabor wavelet with analyzing the best scale and orientation parameters. In addition to the mean and standard deviation on the Gabor filter bank, entropy, contrast and other statistical features are used for classification. Experimental results show that the entropy can better extract texture features on Gabor wavelet, which greatly improve the wood recognition rate.