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  Object correspondence as a machine learning problem

Schölkopf, B., Steinke, F., & Blanz, V. (2005). Object correspondence as a machine learning problem. In S. Dzeroski, L. De Raedt, & S. Wrobel (Eds.), ICML '05: 22nd International Conference on Machine Learning (pp. 776-783). New York, NY, USA: ACM Press.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-D6E5-0 Version Permalink: https://hdl.handle.net/21.11116/0000-0005-2126-B
Genre: Conference Paper

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Locator:
https://dl.acm.org/citation.cfm?doid=1102351.1102449 (Publisher version)
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 Creators:
Schölkopf, B1, 2, Author           
Steinke, F1, 2, Author           
Blanz, V, Author           
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 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.

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 Dates: 2005-08
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: 3386
DOI: 10.1145/1102351.1102449
 Degree: -

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Title: 22nd International Conference on Machine Learning (ICML 2005)
Place of Event: Bonn, Germany
Start-/End Date: 2005-08-07 - 2005-08-11

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Title: ICML '05: 22nd International Conference on Machine Learning
Source Genre: Proceedings
 Creator(s):
Dzeroski, S, Editor
De Raedt, L, Editor
Wrobel, S, Editor
Affiliations:
-
Publ. Info: New York, NY, USA : ACM Press
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 776 - 783 Identifier: ISBN: 1-59593-180-5