Visual quality recognition of nonwovens using generalized Gaussian density model and robust Bayesian neural network

Authors:
Jianli Liu;Baoqi Zuo;Xianyi Zeng;Philippe Vroman;Besoa Rabenasolo
Affiliations:
College of Textiles and Clothing, Jiangnan University, Wuxi 211422, China;College of Textile and Clothing Engineering, Soochow University, Suzhou 215123, China and National Engineering Laboratory for Modern Silk, Soochow University, Suzhou 215123, China;Univ Lille Nord de France, F-59000 Lille, France and ENSAIT, GEMTEX, F-59056 Roubaix, France;Univ Lille Nord de France, F-59000 Lille, France and ENSAIT, GEMTEX, F-59056 Roubaix, France;Univ Lille Nord de France, F-59000 Lille, France and ENSAIT, GEMTEX, F-59056 Roubaix, France
Venue:
Neurocomputing
Year:
2011

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Abstract

This work is dedicated to develop an algorithm for the visual quality recognition of nonwoven materials, in which image analysis and neural network are involved in feature extraction and pattern recognition stage, respectively. During the feature extraction stage, each image is decomposed into four levels using the 9-7 bi-orthogonal wavelet base. Then the wavelet coefficients in each subband are independently modeled by the generalized Gaussian density (GGD) model to calculate the scale and shape parameters with maximum likelihood (ML) estimator as texture features. While for the recognition stage, the robust Bayesian neural network is employed to classify the 625 nonwoven samples into five visual quality grades, i.e., 125 samples for each grade. Finally, we carry out the outlier detection of the training set using the outlier probability and select the most suitable model structure and parameters from 40 Bayesian neural networks using the Occam's razor. When 18 relevant textural features are extracted for each sample based on the GGD model, the average recognition accuracy of the test set arranges from 88% to 98.4% according to the different number of the hidden neurons in the Bayesian neural network.