Of the other approaches, Korn (1988) developed a unifying operator for symbolic repre-
sentation of grey level change. The Susan operator (Smith and Brady, 1997) derives from
an approach aimed to find more that just edges, since it can also be used to derive corners
(where feature boundaries change direction sharply, as in curvature detection in Section 4.8) and
structure-preserving image noise reduction. Essentially, SUSAN derives from smallest univalue
segment assimilating nucleus, which concerns aggregating the difference between elements in
a (circular) template centred on the nucleus. The USAN is essentially the number of pixels
within the circular mask that have similar brightness to the nucleus. The edge strength is then
derived by subtracting the USAN size from a geometric threshold, which is, say, three-quarters
of the maximum USAN size. The method includes a way of calculating edge direction, which
is essential if non-maximum suppression is to be applied. The advantages are in simplicity (and
hence speed), since it is based on simple operations, and the possibility of extension to find
other feature