Morphological Corner Detection
School of Information Technology and Engineering
University of Ottawa
CANADA K1N 6N5
This paper presents a new operator for corner de-
tection. This operator uses a variant of the morpholog-
ical closing operator, which we have called asymmetri-
cal closing. It consists of the successive application of
different morphological tranformations using different
structuring elements. Each of these structuring ele-
ments used to probe the image under study is tuned to
affect corners of different orientation and brightness.
We found that this kind of approach, based on bright-
ness comparisons, leads to better quality results than
others and is achieved at a lower computational cost.
Corners constitute attractive 2D features, often
used in computer vision for tasks such as stereovision,
3D interpretation, motion estimation, and structure
from motion. They abound in indoor scenes where
several polyhedral objects and intersecting planes
(floor, walls, etc.) are present. Corners serve as points
of interest in two-view matching algorithms .
Corner detection is also used in camera calibration for
the localization of reference points on a calibration
pattern ( for example).
Corner detection is sometimes realized through the
analysis of binary edge maps from which chain codes
are extracted in order to find high curvature points
. However, most approaches work directly at
the grayscale level -. These methods usually
use local measurements in order to obtain a corner
strength. Non-maxima suppression and thresholding
lead then to a binary map showing where corners have
been detected. These corner finder are usually char-
acterized by an accuracy of few pixels and a relatively
high level of false positives. Model-based approaches
such as  also exist and allow corner localization
at a subpixel accuracy. But these methods are more
CPU-intensive and are only used after a first corner
map has been obtained.
One of t