CORNER ENHANCEMENT IN CURVATURE SCALE SPACE

摘要

Corner detection is very important for pattern recognition, computer vision and other works. However, successfully extracting corners from a planar curve is difficult because we do not know how to select the appropriate scale for the extraction. In this paper, the problem of corner enhancement via the curve scale space is addressed. The main idea of the approach is to deform the original curve (or construct the curve scale space) and the corners on the deformed curve become distinguished from other structures which eases the process of corner detection. Started from the general geometric heat flow (GGHF), we study under what conditions the GGHF satisfies the scale space causality criteria. This is very important because many different evolution approaches, which have regular scale space properties and other specific properties, can then be found from these conditions. The criteria of corner enhancement are also proposed. Having all these constraints, a new curve evolution scheme which can enhance strong corners suppress noise and satisfies the scale space criteria is presented.