Classification of wood surface texture based on Gauss-MRF model

Abstract

The basal theory of Gauss-MRF is expounded and 2–5 order Gauss-MRF models are established. Parameters of the 2–5 order Gauss-MRF models for 300 wood samples’ surface texture are also estimated by using LMS. The data analysis shows that: 1) different texture parameters have a clear scattered distribution, 2) the main direction of texture is the direction represented by the maximum parameter of Gauss-MRF parameters, and 3) for those samples having the same main direction, the finer the texture is, the greater the corresponding parameter is, and the smaller the other parameters are; and the higher the order of Gauss-MRF is, the more clearly the texture is described. On the condition of the second order Gauss-MRF model, parameter B1, B2 of tangential texture are smaller than that of radial texture, while B3 and B4 of tangential texture are greater than that of radial texture. According to the value of separated criterion, the parameter of the fifth order Gauss-MRF is used as feature vector for Hamming neural network classification. As a result, the ratio of correctness reaches 88%.

Keywords

Wood surface texture / Gauss-MRF / Feature parameter / Parameter estimation, Separation judgment / Classification

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