An approach for evaluating robustness of edge operators using real-world driving scenes

Over the past 20 years there have been many papers that compare and evaluate different edge operators. Most of them focus on accuracy and also do comparisons against synthetic data. This paper focuses on real-world driver assistance scenes and does a comparison based on robustness. The three edge operators compared are Sobel, Canny and the under-publicized phase-based Kovesi-Owens operator. The Kovesi-Owens operator has the distinct advantage that it uses one pre-selected set of parameters and can work across almost any type of scene, where as other operators require parameter tuning. The results from our comparison show that the Kovesi-Owens operator is the most robust of the three, and can get decent results, even under weak illumination and varying gradients in the images.