@inproceedings{3386,
  title = {Object correspondence as a machine learning problem},
  booktitle = {Proceedings of the 22nd International Conference on Machine Learning},
  abstract = {We propose machine learning methods for the estimation of
  deformation fields that transform two given objects into each other, thereby establishing a dense point to point correspondence. The fields are computed using a modified support vector machine
  containing a penalty enforcing that points of one object
  will be mapped to ``similar‘‘ points on the other one. Our system,
  which contains little engineering or domain knowledge, delivers
  state of the art performance. We present application results including close to
  photorealistic morphs of 3D head models.},
  pages = {777-784},
  editors = {L De Raedt and S Wrobel},
  publisher = {ACM},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {New York, NY, USA},
  year = {2005},
  author = {Sch{\"o}lkopf, B. and Steinke, F. and Blanz, V.}
}