Fuzzy inspection of fabric defects based on Particle Swarm Optimization (PSO)

Abstract

A new approach for inspection of fabric defects based on Principal Component Analysis (PCA) and Fuzzy C-Mean Clustering (FCM) Based on Particle Swarm Optimization (PSO) is proposed. First, the PCA is used to reduce the dimension of the original image and computation complexity. The dimension-reduced image features, which can best describe the original image without unnecessary data, are recognized by FCM based on PSO next. The recognition is carried out by the merits of the overall optimizing and higher convergent speed of PSO combined with FCM algorithm, which makes the algorithm have a strong overall searching capacity and avoids the local minimum problems of FCM. At the same time, it reduce the degree of sensitivity of FCM that depends on the initialization values. The results show that the method is more effective than the traditional one with BP neural networks based on wavelet [1,2]. © 2008 Springer-Verlag Berlin Heidelberg.