The algorithm was tested on synthetic and real-life images. The
synthetic images were designed so as to test the detector's ability to
cope with different angles, corner adjacency, gray-level distribution
and noise level [8]. Figure 6a
shows an original synthetic image. Figure 6b shows
its cornerness, and to which extent corners can be identified for
areas of low contrast. Figure 7a shows the product
as an edge
detector. Figure7b shows the orientations of the
corners found. The synthetic image on figure
8a was designed to estimate a measure of the
accuracy of the detector. The corners found on figure
8b were, on average, less than half a pixel
away from their theoretical location, and their orientation was, on
average, within a 1.5 degree error range. The detector's ability to
cope with noise is shown on figure 8c where a
gaussian noise was added to the image before processing
(signal-to-noise ratio SNR = 20 dB). It shows that some corners and
junctions were missed, but the accuracy of the results remains good:
the corners' location is with a .5 pixel error range on average, and
their orientation is within a 2.1 degrees error range.
To demonstrate the capacity of the algorithm to deal with
real-life images, we have applied it to a picture of a part of the
Royal Festival Hall, London, as shown on figure
9a. The corners found are shown on figures
9b and
10a. The potential of the method for edge grouping is illustrated on figure 10b: a simple search algorithm joins corners with compatible directions to obtain grouped contours of the objects.
b) 
Figure 6: a) Original synthetic image. The gradient ramp is linear on top of the image, and quadratic at the bottom of the image. b) Cornerness c(x).
a) 
b) 
Figure 7: a) Edge detector b) Orientations of the corners found. When the angle is narrow, one direction only is given. Note that junctions are also identified.
a) 
b) 
c) 
Figure 8: a) Original synthetic image. b) Orientation of the corners detected. The positions found are on average less than half a pixel away from the theoretical location, and the orientation error is on average less than 1.5 degree. c) Orientation of the corners found on the same image with added gaussian noise (signal-to-noise ratio SNR = 20 dB). Some corners are missed, but the accuracy of the measures found remains good (location error less than .5 pixel; orientation error less than 2.1 degrees)
a) 
b) 
Figure 9: a) Original image of a part of the Royal Festival Hall, London. b) Cornerness c(x).
a) 
b) 
Figure 10: a) Orientations of the detected corners. On a curved corner, the tangential direction is given. b) Joining corners with compatible directions. The knowledge of the orientations of the corners makes grouping edges easy.
