Curvature scale space for image point feature detection

Abstract

This paper describes a new method for image point feature detection based on the curvature scale space (CSS) representation. The first step is to extract edges from the original image using a Canny detector. The corner points of an image are defined as points where image edges have their maxima of absolute curvature. The corner points are detected at a high scale of the CSS image and the locations are tracked through multiple lower scales to improve localization. The curvature zero-crossing points of the edge contours form a different set of image point features. The CSS corner detector is very robust to noise and performed better than three other detectors it was compared to. An improvement to the Canny edge detector's performance is also proposed.