Abstract
The authors propose a new nonparametric dominant point detection algorithm which is divided into two phases: an initial detection phase that locates possible dominant points and a suppression phase that removes redundant dominant points. In the initial detection phase, not only the points with high local curvatures, but also the end points and interception points are detected. In the suppression phase, a novel measurement to act as a suppressing criterion for the removal of the redundant dominant points is proposed. The curvature of a contour segment is modelled by the average cosine angle. If the contour is slightly curved, more points will be suppressed. On the other hand, fewer points will be suppressed if the curved contour is highly curved. The experimental results show that the proposed algorithm can obtain a set of dominant points to represent contours efficiently.
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More From: IEE Proceedings - Vision, Image, and Signal Processing
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