Fast Defect Detection in Textured Surfaces Using 1D Gabor Filters

In this paper, we present a fast machine vision method for the automatic inspection of defects in textured surfaces. Traditional 2D Gabor filtering schemes have been shown to be very effective for detecting local anomalies in textured surfaces of industrial materials. However, they are computationally expensive and sensitive to image rotation. In order to alleviate the limitations of 2D Gabor filtering, we first use a 1D ring-projection transformation to compress a 2D grey-level image into a 1D pattern, and then employ a 1D Gabor filter to detect defects embedded in a homogeneous texture. Given a problem with image size N

× N and filter window W × W, the computational complexity can be reduced significantly from O(W ² N ² ) in the 2D Gabor space to O(WN ² ) in the 1D Gabor space, and the detection results are invariant to rotation changes of a texture. The experiments on structural textures such as a wooden surface, an LCD display, and a machined surface, and statistical textures such as granite, leather, and sandpaper have shown the efficiency and effectiveness of the proposed method.