Object correspondence as a machine learning problem

Schölkopf, B; Steinke, F; Blanz, V

doi:10.1145/1102351.1102449

アイテム詳細

登録内容を編集ファイル形式で保存

一時保存へ追加

タグ情報を表示リリース履歴を表示詳細要約

公開

会議論文

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;
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;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

External Resource

https://dl.acm.org/citation.cfm?doid=1102351.1102449
(出版社版)

Fulltext (restricted access)

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

フルテキスト (公開)

公開されているフルテキストはありません

付随資料 (公開)

There is no public supplementary material available

引用

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.

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

要旨

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.