Comparison Of Algorithms to Detect Straight Line Edges in Images for Metrological Applications

Abstract Edge detection is one of the first steps in analysing a digital image. While several algorithms have been proposed in the past, there is limited work that compares their performance for metrological applications. This work compares seven algorithms including two pixel-based - Otsu and Canny’s methods, and five sub-pixel-based - cubic interpolation, quintic interpolation, moment based, partial-area based and subset tracking based approaches for determining the straight-line edges. The study considers both synthetic and real images. The real images were of objects of interest in dimensional metrology, from razor blade edges to the longitudinal edges of cylinders of various diameters and the parallel measuring faces of gauge blocks. While the root mean squared deviation was the primary criterion, a comparison of the computational times was also carried out. Results indicate that quintic interpolation gives the best results, though it also requires the most computational time among all the evaluated methods.