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  A bootstrapping algorithm for learning linear models of object classes

Vetter, T., Jones, M., & Poggio, T. (1997). A bootstrapping algorithm for learning linear models of object classes. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 40-46). Los Alamitos, CA, USA: IEEE Computer Society.

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 Creators:
Vetter, T1, 2, Author              
Jones, MJ, Author
Poggio, T, Author              
Affiliations:
1Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497797              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794              

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 Abstract: Flexible models of object classes, based on linear combinations of prototypical images, are capable of matching novel images of the same class and have been shown to be a powerful tool to solve several fundamental vision tasks such as recognition, synthesis and correspondence. The key problem in creating a specific flexible model is the computation of pixelwise correspondence between the prototypes, a task done until now in a semiautomatic way. In this paper we describe an algorithm that automatically bootstraps the correspondence between the prototypes. The algorithm -which can be used for 2D images as well as for 3D models-is shown to synthesize successfully a flexible model of frontal face images and a flexible model of handwritten digits.

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 Dates: 1997-06
 Publication Status: Published in print
 Pages: -
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 Rev. Type: -
 Identifiers: DOI: 10.1109/CVPR.1997.609295
BibTex Citekey: 386
 Degree: -

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Title: IEEE Conference on Computer Vision and Pattern Recognition (CVPR 1997)
Place of Event: San Juan, Puerto Rico, USA
Start-/End Date: 1997-06-17 - 1997-06-19

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Title: IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Source Genre: Proceedings
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Publ. Info: Los Alamitos, CA, USA : IEEE Computer Society
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 40 - 46 Identifier: ISBN: 0-8186-7822-4