Object correspondence as a machine learning problem

Schölkopf, B; Steinke, F; Blanz, V; De Raedt S. Wrobel, L.

Item

ITEM ACTIONSEXPORT

Add to Basket

Please note that a newer version of this item is available:
https://pure.mpg.de/pubman/item/item_1791658_3

DetailsSummary

Released

Conference Paper

Object correspondence as a machine learning problem

MPS-Authors

/persons/resource/persons84193

Schölkopf, B
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons84235

Steinke, F
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons83815

Blanz, V
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;

External Resource

No external resources are shared

Fulltext (restricted access)

There are currently no full texts shared for your IP range.

Fulltext (public)

There are no public fulltexts stored in PuRe

Supplementary Material (public)

There is no public supplementary material available

Citation

Schölkopf, B., Steinke, F., & Blanz, V. (2005). Object correspondence as a machine learning problem. In ICML 2005 (pp. 777).

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-D6E5-0

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 ``similaramp;lsquo;amp;lsquo; 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.