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Proceedings Paper

K-AVE + GNN + Sobel = an effective, highly parallel edge detector approach
Author(s): Ka Po Lam; Anthony Furness
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Paper Abstract

Edge detection is an important first step in many vision tasks where its improvements in speed and efficiency present a continuous challenge for developers of high-speed image recognizers. Classical techniques for accurate detection of edge features, as exemplified by Canny operator, demands such expensive operations as the iterative use of Gaussians and Laplacians, and their designs are largely sequential. Alternatively a variety of complex and edge-preserving filters have been developed to reduce the effects of noise without significantly distorting the edge loci. This paper describes a cascaded precursor approach for edge detection based on selective local contrast modifications which combine point- wise image operators and non-linear transformation. A principal advantage of the approach lies in its simplicity and uniformity of operations; the latter is a characteristic blueprint for efficient (parallel) low-level image processing algorithms. Further, unlike many enhancement algorithms, the characteristics of the proposed precursor can be studied analytically, thus allowing the independent adjustments of detector parameters for maximum performance in the specific environment.

Paper Details

Date Published: 19 September 1997
PDF: 9 pages
Proc. SPIE 3166, Parallel and Distributed Methods for Image Processing, (19 September 1997); doi: 10.1117/12.279617
Show Author Affiliations
Ka Po Lam, Univ. of Keele (United Kingdom)
Anthony Furness, Univ. of Keele (United Kingdom)


Published in SPIE Proceedings Vol. 3166:
Parallel and Distributed Methods for Image Processing
Hongchi Shi; Patrick C. Coffield, Editor(s)

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