On the Realization and Analysis of Circular Harmonic Transforms for Feature Detection

Cartesian-separable realizations of circular-harmonic decompositions for angular spectrum estimation are presented and a powerful test-statistic for rotation-invariant feature-detection in images is proposed. The resulting steerable filters with a finite impulse response (FIR) have a low computational complexity. The test statistic is used to detect wedges, i.e. corners of arbitrary angle and unknown orientation, in synthetic and real imagery.