The application of pattern recognition in wood processing industry
Yeqin Wang, Hui Wang · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2010
In order to improve the level of automation in wood production, the parameters of Gray level co-occurrence matrix(GLCM) and Gauss - Markov random field(GMRF) were extracted. S - NFS algorithm was applied to data fusion. And the redundancy and complementarities of two texture parameters were used to build the wood texture parameter system. An integrated measurement rule based on BP neural network classifier's overall recognition rate of samples was advanced to design its integrated classifier. Experiments show that the recognition rate of integrated neural network classifier is superior to the individual network and the nearby classifier, and the average recognition rate of 10 texture samples have reached up to 97%, which could meet the needs of industrial production. And it shows that the established parameter system for wood texture description is effective.