Image Defect Recognition Based on 'Super Fuzzy' Characteristic

In this paper, we propose a new defects recognition algorithm for dynamic image based on “super fuzzy” feature. With this algorithm, the image is divided into some variable windows, and the eigenvector of each window is constructed. We introduce “super fuzzy” vector to make window vectors “super fuzzy” processing, thus the window feature has “super fuzzy” characteristic with the difference of the primary and secondary. Also we present window coefficient to adjust recognition speed and accuracy according to different images. Furthermore, objective function, membership function and clustering center calculation function of fuzzy clustering algorithm with window coefficient and “super fuzzy” vector are proposed in this paper. At last, we take example for fabric defects detection with this algorithm, list recognition results, discuss recognition result influence by “super fuzzy” feature and size change of window, and make some comparison with other algorithms. The conclusion shows that this algorithm can recognize more categories of image abnormal regions with high-accuracy, high-speed, no-training and extensive application.