Uncertainty Propagation in Model-Based Recognition
International Journal of Computer Vision
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It is shown that the set of all paraperspective images with arbitrary reference point and the set of affine images of a 3-D object are identical. Consequently, all uncalibrated paraperspective images of an object can be constructed from a 3-D model of the object by applying an affine transformation to the model and every affine image of the object represents some uncalibrated paraperspective image of the object. It follows that the paraperspective images of an object can be expressed as linear combinations of any two non-degenerate images of the object. When the image position of the reference point is given the parameters of the affine transformation (and, likewise, the coefficients of the linear combinations) satisfy two quadratic constraints. Conversely, when the values of parameters are given the image position of the reference point is determined by solving a bi-quadratic equation.