Wavelet Texture Analysis And Robust Bayesian Neural Network For Visual Quality Recognition Of Nonwovens

To explore the evaluation and inspection of visual quality of nonwovens using intelligent methods, an algorithm is proposed originally that integrates wavelet texture analysis and robust Bayesian neural network to identify the visual quality of nonwovens coming from five grades that are formed with different combing and thermal bonding processes. The final target of this work is to recognize the visual quality grade of nonwovens objectively and precisely, which is realized through three stages, nonwoven image denoising, feature extraction and reorganization by implementing the multiple wavelet basis image denoising using Besov projections algorithm, wavelet texture analysis and robust Bayesian neural network.According the characteristic that the smoothness of nonwoven texture in different domain, the multiple wavelet basis image denoising using Besov projections algorithm is used to eliminate the noise within nonwoven image, and the relationship between the number of wavelet bases and PSNR (peak signal to noise ratio), computation time is also researched deeply from the theoretical and experimental vision. A convergence criterion for multiple wavelet basis image denoising using Besov projections algorithm is proposed that takes the difference of the square sum of the high frequency of wavelet coefficients to be no larger than zero. Compared with denoising method using common thresholding, the proposed method in this paper not only eliminates the noise effectively, e. g. the PSNR is no less than 40 dB, but preserves the character of various smoothness and prevents from over-denoising phenomenon obviously.One of the Tamura textural parameters and the power spectrum of Fourier transform are used to evaluate the contrast and direction of the nonwoven texture, and the results are compared with the ones of the six images of VisTex database. In this research, the texture analysis model of nonwovens is established, and two feature extraction methods are explored deeply based on energy features L1 and L2 of wavelet coefficients and the two control parameters of generalized Gaussian density model, i.e. the scale and shape parameter,κandζ, which can describe and portray the texture of nonwoven image at different scales and from various directions. Additionally, the recognition accuracy R of 1-neighhour classifier is considered as an evaluation parameter of the discrimination of the texture features extracted with the 2 schemes.Finally, with the two types of texture features, the robust Bayesian neural network is implemented to recognize the visual quality of 625 nonwoven samples belonging to 5 different grades. In this part, the structure and design of robust Bayesian neural network, weight optimization, outlier probability estimation and model selection are researched in system, especially the optimization of weights with UCMINF (An Algorithm for Unconstrained, Nonlinear Optimization) algorithm, the structure design method based on inference principle of small samples and structural risk minimization principle, and the optimal model selection with recognition accuracy and the evidence framework theory are also introduced in details, respectively. In the aspects of structure design and model selection of robust Bayesian neural network, the criterion of maximum risk and the standard that combines the reorganization accuracy and log-evidence are explored initiatively. For example, when decomposed at 4 levels with coif3 wavelet base and twelve L1 energy features are used as inputs of the optimal robust Bayesian neural network with 3 hidden neurons, the recognition accuracy of 125 test samples is 99.2%.The research results from the 625 samples from 5 different indicate that the method for visual quality reorganization of nonwovens based on wavelet texture and robust Bayesian neural network has high accuracy and robustness, which is feasible and valid.