Comparing the laplacian zero crossing edge detector with the second directional derivative edge detector

We present evidence that the Laplacian Zero-Crossing operator does not use neighborhood information as effectively as the second directional derivative edge operator. We show that the use of a Gaussian smoother with standard deviation 5.0 for the Laplacian of Gaussian edge operator with a neighborhood size of 50 × 50 both misses and misplaces edges on an aerial image of a mobile home park. Our results of the Laplacian edge detector on a noisy test checkerboard image are also not as good as the second directional derivative edge operator. We conclude by discussing a number of open issues on edge operator evaluation.