Combining multiple similarity metrics for corner matching

Abstract

Corner matching is an important operation in digital image processing and computer vision where it is used for a range of applications including stereo vision and image registration. A number of corner similarity metrics have been developed to facilitate matching, however, any individual metric has a limited effectiveness depending on the content of images to be registered and the different types of distortions that may be present. This paper explores combining corner similarity metrics to produce more effective measures for corner matching. In particular the combination of two similarity metrics is investigated using experiments on a number of images exhibiting different types of transformations and distortions. The results suggest that a linear combination of different similarity metrics may produce more accurate and robust assessments of corner similarity.