A comparison of robust Bayesian and LVQ neural network for visual uniformity recognition of nonwovens

The visual uniformity recognition of nonwoven materials using image analysis and neural network is a typical application of pattern recognition in textile industry. In this paper, we try to find a solution to this problem by combining the generalized Gaussian density (GGD) model in wavelet domain and two types of neural networks, robust Bayesian and learning vector quantization (LVQ) neural network. The proposed model is constituted with two stages, i.e., texture representation and pattern recognition. For texture representation, each image is decomposed into four levels using the 9-7 bi-orthogonal wavelet base. The wavelet coefficients in each subband are independently modelled by the GGD model. Moreover, taken as textural features, the corresponding scale and shape parameters estimated from the wavelet coefficients distribution with the maximum likelihood (ML) estimation are extracted in order to train and test the neural network for visual uniformity classification. During the pattern recognition part, robust Bayesian neural network and LVQ neural network are used as classifier. Especially, the experiments based on robust Bayesian neural network are taken as the key point. Experimental results indicate that the robust Bayesian neural network perform superiorly.