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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 (48). Tübingen, Germany: Max Planck Institute for Biological Cybernetics.

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MPIK-TR-48.pdf (Publisher version), 325KB
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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: 1997-03
 Publication Status: Published in print
 Pages: 11
 Publishing info: Tübingen, Germany : Max Planck Institute for Biological Cybernetics
 Table of Contents: -
 Rev. Type: -
 Identifiers: Report Nr.: 48
BibTex Citekey: 1513
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Title: Technical Report of the Max Planck Institute for Biological Cybernetics
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Pages: - Volume / Issue: 48 Sequence Number: - Start / End Page: - Identifier: -