We describe a framework for registering a group of images together using a set of non-linear diffeomorphic warps. The result of the groupwise registration is an implicit definition of dense correspondences between all of the images in a set, which can be used to construct statistical models of shape change across the set, avoiding the need for manual annotation of training images. We give examples on two datasets (brains and faces) and show the resulting models of shape and appearance variation. We show results of experiments demonstrating that the groupwise approach gives a more reliable correspondence than pairwise matching alone. © Springer-Verlag Berlin Heidelberg 2004.
|Title of host publication
|Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|Lect. Notes Comput. Sci.
|Number of pages
|Published - 2004
|European Conference on Computer Vision - Copenhagen
Duration: 1 Jan 1824 → …
|Lecture Notes in Computer Science
|European Conference on Computer Vision
|1/01/24 → …