Image Corner Detection Based on Curvature Scale Space and Adaptive Thresholding

Abstract: Corner detection or the more general terminology interest point detection is an approach used within computer vision systems to extract certain kinds of features and infer the contents of an image. Corner detection is frequently used in motion detection, image matching, tracking, image mosaicing, panorama stitching, 3D modelling and object recognition. It is difficult to detect both fine and coarse features at the same time using single-scale corner detection whereas multi-scale feature detection is inherently able to solve this problem. This paper describes a multi-scale image corner detection method based on the curvature scale-space (CSS) representation and adaptive thresholding. This method uses an adaptive local curvature threshold instead of a global threshold. To eliminate falsely detected corner, the angles of corners are checked in a dynamic region of support. The results of the proposed method were compared with the results of some other popular corner detection methods. Experimental results show that the proposed corner detection method gives better results compared to other method