Title :
An Affine Resilient Curvature Scale-Space Corner Detector
Author :
Awrangjeb, Mohammad ; Guojun Lu ; Murshed, Manzur
Author_Institution :
Gippsland Sch. of Inf. Technol., Monash Univ., Clayton, Vic., Australia
Abstract :
Curvature scale-space (CSS) corner detectors look for curvature maxima or inflection points on planar curves. They use arc-length parameterized curvature. Therefore, they are not robust to affine transformations since the arc-length of a curve is not preserved under affine transformations. However, the affine-length of a curve is relatively invariant to affine transformations. This paper presents an improved CSS corner detector by applying the affine-length parameterized curvature to the CSS corner detection technique. A thorough robustness study has been carried out on a large database considering a wide range of affine transformations.
Keywords :
feature extraction; image matching; affine transformations; arc-length parameterized curvature; resilient curvature scale-space corner detector; Australia; Cascading style sheets; Databases; Detectors; Image edge detection; Information technology; Kernel; Object detection; Robustness; Shape; Corner detection; curvature scale-space;
Conference_Titel :
Acoustics, Speech and Signal Processing, 2007. ICASSP 2007. IEEE International Conference on
Conference_Location :
Honolulu, HI
Print_ISBN :
1-4244-0727-3
DOI :
10.1109/ICASSP.2007.366137