Automatic detection of welding defects using texture features

In this paper we present a new approach to detecting weld defects from digitalised films based on texture features. Texture is one of the most important features used in recognising patterns in an image. However, these features are not yet commonly exploited in the analysis of X-ray images in NDT. The paper describes two groups of widely used texture features: 1) features based on the cooccurrence matrix, which gives a measurement of how often one grey value will appear in a specified spatial relationship to another grey value on the image; and 2) features based on 2D Gabor functions, i.e., Gaussian shaped band-pass filters, with dyadic treatment of the radial spatial frequency range and multiple orientations, which represent an appropriate choice for tasks requiring simultaneous measurement in both space and frequency domains. The proposed approach to detecting weld defects follows a general pattern recognition scheme based on three steps: segmentation, feature extraction and classification. That is, in our case, 1) potential defects are segmented using an edge detector based on the Laplacian-of-Gauss operator; 2) texture features of the potential defects are extracted; and 3) the most relevant features are used as input data on a statistical classifier. This preliminary study makes a contribution to the improvement of the automatic detection of welding defects.