We describe an algorithm for obtaining correspondences across a group of images of deformable objects. The approach is to construct a statistical model of appearance which can encode the training images as compactly as possible (a Minimum Description Length framework). Correspondences are defined by piece-wise linear interpolation between a set of control points defined on each image. Given such points a model can be constructed, which can approximate every image in the set. The description length encodes the cost of the model, the parameters and most importantly, the residuals not explained by the model. By modifying the positions of the control points we can optimise the description length, leading to good correspondence. We describe the algorithm in detail and give examples of its application to MR brain images and to faces. We also describe experiments which use a recently-introduced specificity measure to evaluate the performance of different components of the algorithm.
|Title of host publication
|BMVC 2005 - Proceedings of the British Machine Vision Conference 2005|BMVC - Proc. Br. Mach. Vis. Conf.
|Place of Publication
|Number of pages
|Published - 2005
|2005 16th British Machine Vision Conference, BMVC 2005 - Oxford
Duration: 1 Jul 2005 → …
|2005 16th British Machine Vision Conference, BMVC 2005
|1/07/05 → …
- Group-wise registration