Issue No. 06 - June (1986 vol. 8)
Vishvjit S. Nalwa , Artificial Intelligence Laboratory and the Information Systems Laboratory, Stanford University, Stanford, CA 94305.
Thomas O. Binford , Artificial Intelligence Laboratory, Stanford University, Stanford, CA 94305.
An edge in an image corresponds to a discontinuity in the intensity surface of the underlying scene. It can be approximated by a piecewise straight curve composed of edgels, i.e., short, linear edgeelements, each characterized by a direction and a position. The approach to edgel-detection here, is to fit a series of one-dimensional surfaces to each window (kernel of the operator) and accept the surfacedescription which is adequate in the least squares sense and has the fewest parameters. (A one-dimensional surface is one which is constant along some direction.) The tanh is an adequate basis for the step-edge and its combinations are adequate for the roof-edge and the line-edge. The proposed method of step-edgel detection is robust with respect to noise; for (step-size/noise) > 2.5, it has subpixel position localization (position < 3) and an angular localization better than 100; further, it is designed to be insensitive to smooth shading. These results are demonstrated by some simple analysis, statistical data, and edgel-images. Also included is a comparison of performance on a real image, with a typical operator (Difference-of-Gaussians). The results indicate that the proposed operator is superior with respect to detection, localization, and resolution.
Vishvjit S. Nalwa, Thomas O. Binford, "On Detecting Edges", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 8, no. , pp. 699-714, June 1986, doi:10.1109/TPAMI.1986.4767852