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Proceedings Paper

Subpixel edge detector using expectation of first-order derivatives
Author(s): Gongzhu Hu; Xing Ping Cao
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Paper Abstract

The basic idea of edge detection is to locate positions where changes of image values (e.g., gray levels) are large. Many edge detection algorithms based on this idea compute derivative of the image function and locate edges at local derivative maxima. One problem is that the local derivative maximum may not be at a precise edge location because it ignores the 'contribution' to the edge from the surrounding pixels where the derivatives are non- maximum. We present in this paper a new edge detection method that locates edges at the probability expectation of first-order derivative in a neighborhood of an edge. This approach enables us to achieve subpixel precision (edges may not be at sample pixels). In addition, use of expectation has noise-reduction effect. Preliminary experiments showed good results produced by the proposed method.

Paper Details

Date Published: 19 May 1992
PDF: 11 pages
Proc. SPIE 1657, Image Processing Algorithms and Techniques III, (19 May 1992); doi: 10.1117/12.58347
Show Author Affiliations
Gongzhu Hu, Central Michigan Univ. (United States)
Xing Ping Cao, Central Michigan Univ. (United States)

Published in SPIE Proceedings Vol. 1657:
Image Processing Algorithms and Techniques III
James R. Sullivan; Benjamin M. Dawson; Majid Rabbani, Editor(s)

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