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Conference Paper

A bootstrapping algorithm for learning linear models of object classes


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

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Vetter, T., Jones, M., & Poggio, T. (1997). A bootstrapping algorithm for learning linear models of object classes. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR 1997) (pp. 40-46). Los Alamitos, CA, USA: IEEE Computer Society.

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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.