A subpixel edge detection method based on an arctangent edge model

The sub-pixel edge detection method is widely applied in image processing to improve accuracy of measurement and recognition. Detection methods often encounter difficulties with low computational efficiency or poor robustness. To address such difficulties, a new least-squared-error-based method is proposed in this paper. First, a one-dimensional solution is derived by means of an arctangent edge model. In the two-dimensional situation, the Sobel operator and the cubic surface fitting method are used to determine the normal direction of edge. Then, two-dimensional edge detection can be transformed into a one-dimensional problem that can be solved with a one-dimensional solution. Because there is no complicated surface fitting in this least-squared-error-based method, it will provide an opportunity to ascertain quickly the accurate location of an edge. The experiment is described at the end of the paper, comparing three edge detection methods. The results indicate that the new detection approach has robustness equal to the traditional least-squared-error-based methods, while run time is much faster and very close to the moment-based methods. The above advantages indicate this approach is very suitable for on-line accurate detection.