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IEEE Trans Pattern Anal Mach Intell. 2010 Nov 30. [Epub ahead of print]

Higher-Dimensional Affine Registration and Vision Applications.

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  • 1University of Florida, Gainesville.


This paper studies affine registration in $\R^m$ with $m$ typically ranging from $4$ to $12$. To justify breaking this dimension barrier, the first part of the paper describes three novel matching problems that can be formulated and solved as affine point-set registration problems in dimensions greater than three: stereo correspondence under motion, image set matching and covariant point-set matching, problems that are not only interesting in their own right but also have potential for important vision applications. Unfortunately, most of the existing affine registration algorithms do not generalize easily to higher dimensions due to their inefficiency. Therefore, the second part of this paper develops a novel algorithm for estimating the affine transform between two point sets in $\mathbb R^m$. Specifically, the algorithm follows the common approach of solving iteratively the correspondences and transform. The initial correspondences are determined using the novel notion of local spectral features, features constructed from local distance matrices. Unlike many correspondence-based methods, the proposed algorithm is capable of registering point sets of different size, and the use of local features provides some degree of robustness against noise and outliers. The proposed algorithm is validated on a variety of synthetic point sets in different dimensions with varying degrees of deformation and noise.

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