On the Realization and Analysis of Circular Harmonic Transforms for Feature Detection
This work addresses computational efficiency and accuracy in feature detection for image processing applications, representing an incremental improvement in method optimization.
The paper tackled the computational complexity of circular-harmonic transforms for local image feature detection by introducing a radial apodization method that exploits polar and Cartesian separability, resulting in reduced discretization artefacts and demonstrated utility in detecting corners from real airborne sensor data with a test-statistic accounting for noise and clutter.
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.