Automatic texture defect detection using Gaussian mixture entropy modeling

In this paper we propose a new unsupervised, automated texture defect detection that does not require any user-inputs and yields high accuracies at the same time. To achieve this end we use the non-extensive entropy with Gaussian gain as the regularity index, computed locally from texture patches through a sliding window approach. The optimum window size is determined by modeling the entropy values by a two-mode Gaussian mixture model and checking for the minimum entropy of the mode-probabilities. The outlier entropy values corresponding to defective areas are defined as those that exceed thrice the standard deviation, as is the norm in statistics. The result is automatic defect detection with no manual intervention. Empirical results on defective texture images from the Brodatz database provide accurate localization of the defect as compared to Chetverikov and Hanbury's maximal regularity method, which requires manual setting of threshold parameters for each type of texture despite of being a benchmark for texture defect detection.