Corner Detection Based on a Dynamic Measure of Cornerity

Yang Zhang, Baojiang Zhong, Xun Sun

Abstract: Existing contour-based corner detectors generally identify corners from a contour curve by measuring the cornerity of each point (i.e., the confidence to be a corner) with a fixed-radius region of support (RoS), and thus could yield inferior performance due to low adaptivity to local structures of the input curve. To overcome the difficulty, a novel cornerity measure based on a dynamic RoS is proposed in this paper, with which an efficient corner detector is developed. For a given point on the curve, the dynamic RoS is constructed with two straight-line arms stretching towards both sides along the curve, under a pre-determined error tolerance imposed on the average perpendicular distance from the curve to each arm within its stretching range. Then, our cornerity model is established based on the lengths of the two arms and the angle between them, which is then exploited to evaluate whether the current point is a corner or not via a cornerity thresholding. Extensive experimental results show that the proposed corner detector can deliver superior performance and exhibit higher robustness over the existing state-of-the-arts.