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A Bootstrapping Algorithm for Learning Linear Models of Object Classes

MPG-Autoren
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Vetter,  T
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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MPIK-TR-48.pdf
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Zitation

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.


Zitierlink: https://hdl.handle.net/11858/00-001M-0000-0013-EA62-6
Zusammenfassung
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.