Curvature Scale Space Based Image Corner Detection
Farzin Mokhtarian and Riku Suomela
Centre for Vision, Sp eech, and Signal Pro cessing
Department of Electronic and Electrical Engineering
University of Surrey, Guildford, England GU2 5XH, UK
e-mail: [email protected]. uk
ABSTRACT quality of its results. Note however that the Canny de-
tector is not a critical part of our corner detector: it can
This pap er describ es a new metho d for image corner
b e replaced with another edge detector if the new edge
detection based on the curvature scale space (CSS) rep-
detector is b elieved to p erform b etter.
resentation. The rst step is to extract edges from the
Section 2 gives a brief overview of the CSS metho d
original image using a Canny detector. The Canny de-
and section 3 describ es the prop osed corner detector.
tector sometimes leaves a gap in T-junctions so during
The p erformance of a corner detector is b est evaluated
edge extraction, the gaps are examined to lo cate the
with real test images and in section 4 the results of the
T-junction corner p oints. The corner p oints of an im-
CSS corner detector are presented. Four images with
age are de ned as p oints where image edges have their
di erent prop erties are used in the exp eriments.
maxima of absolute curvature. The corner p oints are de-
tected at a high scale of the CSS and the lo cations are
2 The curvature scale space technique
tracked through multiple lower scales to improve lo caliz-
ation. The nal stage is to compare T-junction corners
The curvature scale space technique is suitable for re-
to CSS corners and remove duplicates. This metho d
covering invariant geometric features (curvature zero-
is very robust to noise and we b elieve that it p erforms
crossing p oints and/or extrema) of a planar curve at
b etter than the existing corner detectors.
multiple scales. To compute it, the curve is rst para-
metrized by the arc length parameter u:
1 Intro duction