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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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https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=609295
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Zitation

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.


Zitierlink: https://hdl.handle.net/11858/00-001M-0000-0013-EA1C-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.