Abstract
This paper describes a new method for image corner 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 Canny detector sometimes leaves a gap in T-junctions so during edge extraction, the gaps are examined to locate the T-junction corner points. 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 and the locations are tracked through multiple lower scales to improve localization. The final stage is to compare T-junction corners to CSS corners and remove duplicates. This method is very robust to noise and we believe that it performs better than the existing corner detectors.
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