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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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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-EA62-6 Version Permalink: https://hdl.handle.net/21.11116/0000-0005-E3DC-3
Genre: Report

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MPIK-TR-48.pdf (Publisher version), 325KB
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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: 1997-03
 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
 Degree: -

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Title: Technical Report of the Max Planck Institute for Biological Cybernetics
Source Genre: Series
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Pages: - Volume / Issue: 48 Sequence Number: - Start / End Page: - Identifier: -