Camera model selection based on geometric AIC

The problem of selecting a camera model is addressed here using the Geometric AIC (Akaike Information Criterion) proposed by Kanatani, which considers both the residual of the data fitting to the model as well as the complexity of the model. Camera models describe the geometrical relation between the SD location of object points and the image location of their projections. The most commonly used camera models ale the projective/perspective camera model and the affine camera model. Intuitively, the projective camera model, which is nonlinear and is characterized by more parameters, models the imaging geometry better, but also, is believed to lead to numerically less stable solutions. The affine camera model, which Is an approximation to the projective camera model with less parameters, is recommended to be used when the object depth is much smaller than the object distance. However, there a's no quantitative criterion for the decision: which camera model should be used, projective or affine? In this paper, the Geometric AIC criterion is used for deciding between the two camel cl models it-a the contest of two tasks: estimating the projection matrix from SD and cor responding PD data, and estimating the fundamental matrix from two sets of 2D data. It is found that in most cases, it is the projective camera model which is more appropriate. Still, in the cases where the affine camera model is traditionally used, the measures of appl appropriateness of the two models are roughly the same (with a! small advantage to the affine camera model).