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.
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