Robust image corner detection through curvature scale space

1 January 1998

journal article
Published by Institute of Electrical and Electronics Engineers (IEEE)

Vol. 20 (12) , 1376-1381
https://doi.org/10.1109/34.735812

Abstract

This paper describes a novel 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 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 tracked through multiple lower scales to improve localization. This method is very robust to noise, and we believe that it performs better than the existing corner detectors. An improvement to Canny edge detector's response to 45° and 135° edges is also proposed. Furthermore, the CSS detector can provide additional point features (curvature zero-crossings of image edge contours) in addition to the traditional corners.

Keywords

This publication has 24 references indexed in Scilit:

Model based corner detection
Published by Institute of Electrical and Electronics Engineers (IEEE) ,2002
A practical solution to corner detection
Published by Institute of Electrical and Electronics Engineers (IEEE) ,2002
Ridge's corner detection and correspondence
Published by Institute of Electrical and Electronics Engineers (IEEE) ,2002
Fast corner detection
Image and Vision Computing, 1998
A mean field annealing approach to robust corner detection
IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 1998
Boundary-based corner detection using neural networks
Pattern Recognition, 1997
Corner detection in natural images based on the 2-D Hilbert transform
Signal Processing, 1996
Optimal corner detector
Computer Vision, Graphics, and Image Processing, 1989
Local ordered grey levels as an aid to corner detection
Pattern Recognition, 1984
Filtered projections as an aid in corner detection
Pattern Recognition, 1983