Rotation-invariant object recognition using edge profile clusters

This paper introduces a new method to recognize objects at any rotation using clusters that represent edge profiles. These clusters are calculated from the Interlevel Product (ILP) of complex wavelets whose phases represent the level of “edginess” vs “ridginess” of a feature, a quantity that is invariant to basic affine transformations. These clusters represent areas where ILP coefficients are large and of similar phase; these are two properties which indicate that a stable, coarse-level feature with a consistent edge profile exists at the indicated locations. We calculate these clusters for a small target image, and then seek these clusters within a larger search image, regardless of their rotation angle. We compare our method against SIFT for the task of rotation-invariant matching in the presence of heavy Gaussian noise, where our method is shown to be more noise-robust. This improvement is a direct result of our new edge-profile clusters' broad spatial support and stable relationship to coarse-level image content.