We seek to automatically establish dense correspondences across groups of images. Existing non-rigid registration methods usually involve local optimisation and thus require accurate initialisation. It is difficult to obtain such initialisation for images of complex structures, especially those with many self-similar parts. In this paper we show that satisfactory initialisation for such images can be found by a parts+geometry model. We use a population based optimisation strategy to select the best parts from a large pool of candidates. The best matches of the optimal model are used to initialise a groupwise registration algorithm, leading to dense, accurate results. We demonstrate the efficacy of the approach on two challenging datasets, and report on a detailed quantitative evaluation of its performance. © 2010 Springer-Verlag.
CITATION STYLE
Zhang, P., Adeshina, S. A., & Cootes, T. F. (2010). Automatic learning sparse correspondences for initialising groupwise registration. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6362 LNCS, pp. 635–642). https://doi.org/10.1007/978-3-642-15745-5_78
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