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Article

Object correspondence as a machine learning problem

Authors:

Bernhard Schölkopf,

Florian Steinke,

Volker BlanzAuthors Info & Claims

ICML '05: Proceedings of the 22nd international conference on Machine learning

Pages 776 - 783

https://doi.org/10.1145/1102351.1102449

Published: 07 August 2005 Publication History

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.

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Cited By

Peter LAlexander DMagnain CIglesias J(2021)Uncertainty-Aware Annotation Protocol to Evaluate Deformable Registration AlgorithmsIEEE Transactions on Medical Imaging10.1109/TMI.2021.307084240:8(2053-2065)Online publication date: Aug-2021
https://doi.org/10.1109/TMI.2021.3070842
Luthi MGerig TJud CVetter T(2018)Gaussian Process Morphable ModelsIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2017.273974340:8(1860-1873)Online publication date: 1-Aug-2018
https://doi.org/10.1109/TPAMI.2017.2739743
Jud CMori NCattin P(2016)Sparse Kernel Machines for Discontinuous Registration and Nonstationary Regularization2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)10.1109/CVPRW.2016.63(449-456)Online publication date: Jun-2016
https://doi.org/10.1109/CVPRW.2016.63
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Object correspondence as a machine learning problem
1. Computing methodologies

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Information

Published In

cover image ACM Other conferences

ICML '05: Proceedings of the 22nd international conference on Machine learning

August 2005

1113 pages

ISBN:1595931805

DOI:10.1145/1102351

General Chair:
Saso Dzeroski
Jozef Stefan Institute, Slovenia
,
Program Chairs:
Luc De Raedt,
Stefan Wrobel

Copyright © 2005 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 August 2005

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Cited By

Peter LAlexander DMagnain CIglesias J(2021)Uncertainty-Aware Annotation Protocol to Evaluate Deformable Registration AlgorithmsIEEE Transactions on Medical Imaging10.1109/TMI.2021.307084240:8(2053-2065)Online publication date: Aug-2021
https://doi.org/10.1109/TMI.2021.3070842
Luthi MGerig TJud CVetter T(2018)Gaussian Process Morphable ModelsIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2017.273974340:8(1860-1873)Online publication date: 1-Aug-2018
https://doi.org/10.1109/TPAMI.2017.2739743
Jud CMori NCattin P(2016)Sparse Kernel Machines for Discontinuous Registration and Nonstationary Regularization2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)10.1109/CVPRW.2016.63(449-456)Online publication date: Jun-2016
https://doi.org/10.1109/CVPRW.2016.63
Vestner MRodolà EWindheuser TBulò SCremers D(2016)Applying Random Forests to the Problem of Dense Non-rigid Shape CorrespondencePerspectives in Shape Analysis10.1007/978-3-319-24726-7_11(231-248)Online publication date: 1-Oct-2016
https://doi.org/10.1007/978-3-319-24726-7_11
Pandya HMadhava Krishna KJawahar C(2015)Servoing across object instances: Visual servoing for object category2015 IEEE International Conference on Robotics and Automation (ICRA)10.1109/ICRA.2015.7140042(6011-6018)Online publication date: May-2015
https://doi.org/10.1109/ICRA.2015.7140042
Jud CLüthi MAlbrecht TSchönborn SVetter T(2014)Variational Image Registration Using Inhomogeneous RegularizationJournal of Mathematical Imaging and Vision10.1007/s10851-014-0497-050:3(246-260)Online publication date: 1-Nov-2014
https://dl.acm.org/doi/10.1007/s10851-014-0497-0
Patanè G(2013)Multi-resolutive sparse approximations of d-dimensional dataComputer Vision and Image Understanding10.1016/j.cviu.2012.10.012117:4(418-428)Online publication date: 1-Apr-2013
https://dl.acm.org/doi/10.1016/j.cviu.2012.10.012
Lüthi MJud CVetter T(2013)A Unified Approach to Shape Model Fitting and Non-rigid RegistrationProceedings of the 4th International Workshop on Machine Learning in Medical Imaging - Volume 818410.1007/978-3-319-02267-3_9(66-73)Online publication date: 22-Sep-2013
https://dl.acm.org/doi/10.1007/978-3-319-02267-3_9
Ahn JKim I(2012)Learning the dynamics of objects by optimal functional interpolationNeural Computation10.1162/NECO_a_0032524:9(2457-2472)Online publication date: 1-Sep-2012
https://dl.acm.org/doi/10.1162/NECO_a_00325
Lüthi MJud CVetter T(2011)Using landmarks as a deformation prior for hybrid image registrationProceedings of the 33rd international conference on Pattern recognition10.5555/2039976.2039999(196-205)Online publication date: 31-Aug-2011
https://dl.acm.org/doi/10.5555/2039976.2039999
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