Object correspondence as a machine learning problem

Schölkopf, B; Steinke, F; Blanz, V

doi:10.1145/1102351.1102449

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Object correspondence as a machine learning problem

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

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

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https://dl.acm.org/citation.cfm?doid=1102351.1102449
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Citation

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.

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.