Comparative evaluation of linear edge detection methods

A comparative evaluation of the most commonly used linear methods for edge detection in grayscale images are presented. Detectors based on the first and second derivatives of image brightness are considered. The method for automatic edge tracking in grayscale images is proposed. The model for assessing errors and artifacts caused by sampling during digitization of real input images is proposed. Investigation of edge detectors isotropy and errors caused by input images sampling is conducted. The advantage of the Isotropic operator for edge tracking is shown. The noise immunity of linear edge detection methods is assessed and the superiority of 3 × 3 gradient operators for noisy images is shown. Isotropic and Sobel operators are identified to be optimal on a basis of sampling errors, output noise level, and computational complexity.