The Community for Technology Leaders
Green Image
Issue No. 06 - June (1986 vol. 8)
ISSN: 0162-8828
pp: 699-714
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.

V. S. Nalwa and T. O. Binford, "On Detecting Edges," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 8, no. , pp. 699-714, 1986.
162 ms
(Ver 3.3 (11022016))