Research on Classification of Wood Surface Texture Based on Markov Random Field

摘要：

To classify wood by surface texture, rank-2 Gibbs-MRF model was established at first. Rank-2 Gibbs-MRF parameters (β2～β9) of 300 wood texture samples were estimated by least square method. Trough data analysis, conclusions were drawn as follow: parameters of different sort of wood texture present a scattered distribution; every Gibbs-MRF parameter denotes the intensity of certain texture cluster type; the more delicate the texture is, the larger the corresponding parameter is, contrarily the smaller. Parameter β2 of radial texture is the maximum one, and β2～β5>0, β6～β9<0; β2 of tangential texture is the minimum one, and β2<0, β3～β9>0; β6～β9 of tangential texture is greater than that of radial texture. According to the separable criterion value of Gibbs-MRF parameters, seven parameters (β2～β7, β9) were selected by Simulated Annealing Algorithm as inputs to BP Neural Networks to classify six sorts of wood texture. As a whole, the correct ratio of classification reaches 84.5％. Conclusions: the seven Gibbs-MRF parameters are valid to describe wood texture feature; it is feasible to classify wood by surface texture according to the seven parameters.

作者: Xuebing BAI Keqi WANG

作者单位: Northeast Forestry University, China

会议类型: 国际会议

会议名称: 2nd IEEE Conference on Industrial Electronics and Applications(ICIEA 2007)(第二届IEEE工业电子与应用国际会议)

会议地点: 哈尔滨

会议语种:英文

在线出版日期: 2007-05-23（万方平台首次上网日期，不代表论文的发表时间）