Flexible Corner Detection Based On A Single-Parameter Control
Shiuh-Yung Chen, Ming-Yang Chern · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1988
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.