Scale-based detection of corners of planar curves

Abstract

A technique for detecting and localizing corners of planar curves is proposed. The technique is based on Gaussian scale space, which consists of the maxima of absolute curvature of the boundary function presented at all scales. The scale space of isolated simple and double corners is analyzed to investigate the behavior of scale space due to smoothing and interactions between two adjacent corners. The scale space is transformed into a tree which provides simple but concise representation of corners at multiple scales. A multiple-scale corner detection scheme is developed using a coarse-to-fine tree parsing technique. The parsing scheme is based on a stability criterion which states that the presence of a corner must concur with a curvature maximum observable at a majority of scales. Experimental results show that the scale-space corner detector is reliable for objects with multiple-size features and noisy boundaries and that it compares favorably with other corner detectors tested.