Author_Institution :
Machine Vision International, Ann Arbor, MI 48104.
Abstract :
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 a Gaussian edge operator with a neighborhood size of 50 Ã 50 both misses and misplaces edges on an aerial image of a mobile home park. Contrary to Grimson and Hildreth´s results, 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.
Keywords :
Computer errors; Detectors; Filters; Gaussian noise; Image edge detection; Laplace equations; Machine vision; Pixel; Smoothing methods; Testing;
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on
DOI :
10.1109/TPAMI.1985.4767629