  Circular-harmonic spectra are a compact representation of local image
features in two dimensions. It is well known that the computational complexity
of such transforms is greatly reduced when polar separability is exploited in
steerable filter-banks. Further simplifications are possible when Cartesian
separability is incorporated using the radial apodization (i.e. weight, window,
or taper) described here, as a consequence of the Laguerre/Hermite
correspondence over polar/Cartesian coordinates. The chosen form also mitigates
undesirable discretization artefacts due to angular aliasing. The possible
utility of circular-harmonic spectra for the description of simple features is
illustrated using real data from an airborne electro-optic sensor. The spectrum
is deployed in a test-statistic to detect and characterize corners of arbitrary
angle and orientation (i.e. wedges). The test-statistic considers uncertainty
due to finite sampling and clutter/noise.
