Object recognition in the presence of changing scale and orientation requires mechanisms to deal with the corresponding feature transformations. Using Gabor wavelets as example, we approach this problem in a correspondence-based setting. We present a mechanism for finding feature-to-feature matches between corresponding points in pairs of images taken at different scale and/or orientation (leaving out for the moment the problem of simultaneously finding point correspondences). The mechanism is based on a macro-columnar cortical model and dynamic links. We present tests of the ability of finding the correct feature transformation in spite of added noise.
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