TY - JOUR
AU - Burn, Stewart
AB - 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.
TI - Edge Detection in Pipe Images Using Classification of Haar Wavelet Transforms
JO - Applied Artificial Intelligence
DO - 10.1080/08839514.2014.927689
DA - 2014-08-09
UR - https://www.deepdyve.com/lp/taylor-francis/edge-detection-in-pipe-images-using-classification-of-haar-wavelet-HLGollO6t3
SP - 675
EP - 689
VL - 28
IS - 7
DP - DeepDyve
ER -