Automated inspection of textured web materials using real Gabor functions

摘要

Quality assurance is the key for increasing competition in the market place. This paper presents a new machine vision based approach for the detection of defects using real Gabor functions. A bank of real Gabor functions, followed by a nonlinear function, is used to sample texture features at different scales. These texture features are compared with those from defect-free (reference) image, and a set of feature difference arrays are created. These are used to generate a combined image output using image fusion. This combined image output is used to obtain a binary image of defects using calibration. This paper also details a new method for automated selection of the center frequency of Gabor function using spectral analysis. Experimental results have confirmed the usefulness of the proposed approach for the automated inspection of textile webs.