Improving the Canny Edge Detector Using Automatic Programming
Lars Vidar Magnusson, Roland Olsson · 2016
In this paper, we employ automatic programming, a relatively unknown evolutionary computation strategy, to improve the non-max suppression step in the popular Canny edge detector. The new version of the algorithm has been tested on a dataset widely used to benchmark edge detection algorithms. The performance has increased by 1.9%, and a pairwise student-t comparison with the original algorithm gives a p-value of 6.45 x 10-9. We show that the changes to the algorithm have made it better at detecting weak edges, without increasing the computational complexity or changing the overall design.