Refining edges detected by a LoG operator

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Abstract
The Laplacian of Gaussian (LoG) operator is one of the most popular operators used in edge detection. This operator, however, has some problems: zero-crossings do not always correspond to edges, and edges with an asymmetric profile introduce a symmetric bias between edge and zero-crossing locations. The authors offer solutions to these two problems. First, for one-dimensional signals, such as slices from images, they propose a simple test to detect true edges, and, for the problem of bias, they propose different techniques: the first one combines the results of the convolution of two LoG operators of different deviations, whereas the others sample the convolution with a single LoG filter at two points besides the zero-crossing. In addition to localization, these methods allow them to further characterize the shape of the edge. The authors present an implementation of these techniques for edges in 2-D images.

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