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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).

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

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 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.

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 Datum: 1997-03
 Publikationsstatus: Erschienen
 Seiten: -
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: -
 Identifikatoren: Reportnr.: 48
BibTex Citekey: 1513
 Art des Abschluß: -

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