A Novel Corner Detector using a Non-cornerness Measure

Abstract

In this paper, a corner detection method based on a new non-cornerness measure is presented. Rather than evaluating local gradients or surface curvatures, as done in previous approaches, a non-cornerness function is developed that can identify stable corners by testing an image region against a set of desirable corner criteria. The non-cornerness function is comprised of two steps: 1) eliminate any pixel located in a flat region and 2) remove any pixel that is positioned along an edge in any orientation. A pixel that passes the non-cornerness test is considered a reliable corner. The proposed method also adopts the idea of non-maximum suppression to remove multiple corners from the results of the non-cornerness function. The proposed method is compared with previous popular methods and is tested with an artificial test image covering several corner forms and three real-world images that are universally used by the community to evaluate the accuracy of corner detectors. The experimental results show that the proposed method outperforms previous corner detectors with respect to accuracy, and that it is suitable for real-time processing.