Ullman and Basri~[1] have shown theoretically, that a three-dimensional object can be represented by a linear combination of two-dimensional images of the object. But they have applied their calculations to artificially created images only, like line drawings of cars. The application to images of real objects turns out to be difficult, because a crucial point of their algorithm is the knowlegde of correspondences in the sample views. In this article we describe a biologically inspired system which automatically provides correspondences between views of a three-dimensional object. This enables us to apply Ullman and Basri's linear combination approach to images of arbitrary, real objects. We give detailed formula of our linear combinations and examples for reconstructed object views. [1] S. Ullman and R. Basri, \emph{Recognition by Linear Combinations of Models}, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 13, No. 10, pp. 992-1006, 1991.
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