Previous work has shown that the uniformity recognition of nonwoven can be considered as a special case of pattern recognition. In this paper, a generalized frame for uniformity recognition based on computer vision and pattern recognition is introduced briefly. To validate the proposed generalized frame, a case study id carried out in experiment. In the experiment section, the uniformity recognition of nonwovens will be solved by unifying wavelet texture analysis, generalized Gaussian density (GGD) model and learning vector quantization (LVQ) neural network. 625 nonwoven images of 5 different uniformity grades, 125 of each grade, are decomposed at four levels with five different wavelet bases of Symlets family. And wavelet coefficients in each subband are independently modeled by the GGD model, while the scale and shape parameters of GGD model are extracted using maximum likelihood (ML) estimator as features to train and test LVQ neural network. For comparison, two energy-based features are also extracted from wavelet coefficients directly and jointly used as textural features. Experimental results coming from 625 nonwoven samples indicate the GGD parameters are more expressive and powerful in characterizing textures than the energy-based ones, especially when the number of decomposition levels is 4.