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Automatic image interpretation for pipe inspection is a relatively recent area of research, which has great potential benefit. An important component of such systems is crack detection, or, more generally, edge or discontinuity detection. This paper describes a new approach to edge detection and applies it to pipe images. The method labels each pixel in an image as an edge pixel or a nonedge pixel by processing the Haar wavelet transform of the image in a window about the pixel using a support vector machine. As a pixel classifier, to within a moderate morphological tolerance, the detector has an accuracy of 99% on the images on which it has been tested and compares favorably with the commonly used Canny edge detector.

This paper presents a new approach to detecting features in pipe images based on a generalisation of the erosion operation. The pipe images can be segmented using support vector machine or other method. The binary image obtained in this way contains a principal connected component made up from the pipe flow lines, the pipe joints and adjoining defects. The morphological analysis allows the principal component of the segmented image to be decomposed into its components. Generalisations of the dilation and erosion operations called α-dilation and α-erosion are defined. Some simple properties of these operations are derived.

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