Automatic Learning Sparse Correspondences for Initialising Groupwise Registration
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.
Pei Zhang Steve A.Adeshina Timothy F.Cootes
Imaging Science and Biomedical Engineering, The University of Manchester, UK
国际会议
北京
英文
635-642
2010-09-01（万方平台首次上网日期，不代表论文的发表时间）