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Proceedings Paper

Flexible Corner Detection Based On A Single-Parameter Control
Author(s): Shiuh-Yung Chen; Ming-Yang Chern
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Paper Abstract

Corner detection is often an important part of feature extraction and pattern recognition. For a given contour image, different sets of corners can be extracted depending on the scale adopted to examine the object. Existing algorithms do not emphasize the adjustability of the detection and the effect of changing their parameters is hard to predict. In this paper, we propose an algorithm which is controlled by a single parameter for corner detection. The tangent direction along the contour is evaluated based on the Poisson function weighted average of the directions connecting the given point to its neighbours within a range specified by the parameter. And the change in the tangent direction is then smoothed and compared within the range to find the corners. Based on our scheme, the number of corners decreases monotonically as the parameter value increases. The scaling effect of this simple parameter is easily predictable and similar to human visual perception. Some experimental results are shown in this article.

Paper Details

Date Published: 19 February 1988
PDF: 7 pages
Proc. SPIE 0848, Intelligent Robots and Computer Vision VI, (19 February 1988); doi: 10.1117/12.942726
Show Author Affiliations
Shiuh-Yung Chen, Northwestern University (United States)
Ming-Yang Chern, Northwestern University (United States)


Published in SPIE Proceedings Vol. 0848:
Intelligent Robots and Computer Vision VI
David P. Casasent; Ernest L. Hall, Editor(s)

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