The Bayesian operating point of the Canny edge detector.
We have investigated the operating point of the Canny edge detector which minimizes the Bayes risk of misclassification. By considering each of the sequential stages which constitute the Canny algorithm, we conclude that the linear filtering stage of Canny, without postprocessing, performs very poorly by any standard in pattern recognition and achieves error rates which are almost indistinguishable from a priori classification. We demonstrate that the edge detection performance of the Canny detector is due almost entirely to the postprocessing stages of nonmaximal suppression and hysteresis thresholding.
Zhang
Yang
Y
Department of Electronic and Electrical Engineering, University of Sheffield, Sheffield S1 3JD, UK. hegallis@gmail.com
Rockett
Peter I
PI
D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D003198:Computer Simulation; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D012372:ROC Curve; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity