Robust image modeling for classification of surface defects on wood boards

The classification of surface defects on wood boards using random field models is presented. The gray levels of images of wood boards are assumed to be governed by causal autoregressive models. The parameters of the model equations are used to form a feature vector that characterizes the board images. These parameters are estimated by two robust algorithms: an algorithm that combines the data cleaning procedure and the two-dimensional M-estimation method, and an algorithm based on the two-dimensional generalized M-estimation method. The estimated parameters are used in the classification of sample boards into the nine classes: eight types of surface defect, and clear wood (no defects). The construction of the hierarchical tree classifiers is described. The experimental testing of the constructed classifiers is described. The accuracy of the classification is discussed