Deformation Invariant Image Matching by Spectrally Controlled Diffeomorphic Alignment

Abstract

We present a new approach to deformation invariant image matching. Our matcher (a) aligns templates to targets over a broad range of nonlinear deformations, (b) factors the total deformation into spectral categories, where low wavenumber deformations are smooth and global and high wavenumbers are turbulent and local, and (c) weighs the reduction in template-target misfit within each category to differentiate between relevant and irrelevant deformations. It accomplishes this by aligning images in a scale-cascaded fashion, with more complex, local deformations following simpler, more global ones. Each step of the cascade involves finding an iterative solution to a nonlinear optimization problem using a Gabor deformation basis. Cascaded alignment makes deformation invariant matching feasible and efficient. Our approach is applied to recognize the flexible bodies of salamanders from a large database; results indicate that the method is very promising.