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.   Previous attempts have been made to improve the filter stage of the Canny algorithm using evolutionary computation, but, to our knowledge, this is the first time it has been used to improve the non-max suppression algorithm.   The fact that we have found a heuristic improvement to the algorithm with significantly better performance on a dedicated test set of natural images suggests that our method should be used as a standard part of image analysis platforms, and that our methodology could be used to improve the performance of image analysis algorithms in general.
['Lars Vidar Magnusson', 'Roland Olsson']
Improving the Canny Edge Detector Using Automatic Programming: Improving Non-Max Suppression