Name
Abstract | ||
|---|---|---|
Affine registration has a long and venerable history in computer vision literature, and in particular, extensive work has been done for affine registration in {\hbox{\rlap{I}\kern 2.0pt{\hbox{R}}}}^2 and {\hbox{\rlap{I}\kern 2.0pt{\hbox{R}}}}^3. This paper studies affine registration in {{\hbox{\rlap{I}\kern 2.0pt{\hbox{R}}}}}^m with m typically ranging from 4 to 12. To justify breaking of 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 {\hbox{\rlap{I}\kern 2.0pt{\hbox{R}}}}^m. Specifically, the algorithm follows the common approach of iteratively solving 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, and the paper also shows experimentally that several instances of the aforementioned three matching problems can indeed be solved satisfactorily using the proposed affine registration algorithm. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/TPAMI.2010.219 | IEEE transactions on pattern analysis and machine intelligence |
Keywords | Field | DocType |
vision applications,affine point-set registration problem,existing affine registration algorithm,paper studies affine registration,affine registration,covariant point-set matching,point set,higher-dimensional affine registration,novel algorithm,proposed affine registration algorithm,matching problem,proposed algorithm,computer vision,point matching,three dimensional,affine transformation,affine transform,vectors,pixel,mathematical model,image registration | Affine transformation,Affine shape adaptation,Computer vision,Point set registration,Affine space,Harris affine region detector,Pattern recognition,Affine combination,Computer science,Affine coordinate system,Artificial intelligence,Affine hull | Journal |
Volume | Issue | ISSN |
33 | 7 | 1939-3539 |
Citations | PageRank | References |
5 | 0.43 | 42 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| S. M. Nejhum Shahed | 1 | 5 | 0.43 |
| Yu-Tseh Chi | 2 | 41 | 2.30 |
| Jeffrey Ho | 3 | 2190 | 101.78 |
| Yang Ming-Hsuan | 4 | 15303 | 620.69 |