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Learning the Topology of Object Views

Jan WieghardtContact Information, Rolf P. WürtzContact Information and Christoph von der Malsburg8, 9

(7)  SIEMENS AG, CT SE 1, Otto-Hahn-Ring 6, D-81730 München, Germany
(8)  Institut für Neuroinformatik, Ruhr-Universität Bochum, D-44780 Bochum, Germany
(9)  Laboratory for Computational and Biological Vision, University of Southern California, Los Angeles, USA
Abstract
A visual representation of an object must meet at least three basic requirements. First, it must allow identification of the object in the presence of slight but unpredictable changes in its visual appearance. Second, it must account for larger changes in appearance due to variations in the object’s fundamental degrees of freedom, such as, e.g., changes in pose. And last, any object representation must be derivable from visual input alone, i.e., it must be learnable.
We here construct such a representation by deriving transformations between the different views of a given object, so that they can be parameterized in terms of the object’s physical degrees of freedom. Our method allows to automatically derive the appearance representations of an object in conjunction with their linear deformation model from example images. These are subsequently used to provide linear charts to the entire appearance manifold of a three-dimensional object. In contrast to approaches aiming at mere dimensionality reduction the local linear charts to the object’s appearance manifold are estimated on a strictly local basis avoiding any reference to a metric embedding space to all views. A real understanding of the object’s appearance in terms of its physical degrees of freedom is this way learned from single views alone.

Keywords  Object recognition - pose estimation - view sphere - correspondence maps - learning


Contact Information Jan Wieghardt
Email: jan.wieghardt@mchp.siemens.de

Contact Information Rolf P. Würtz
Email: rolf.wuertz@neuroinformatik.ruhr-uni-bochum.de
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