  We revisit the problem of model-based object recognition for intensity images
and attempt to address some of the shortcomings of existing Bayesian methods,
such as unsuitable priors and the treatment of residuals with a non-robust
error norm. We do so by using a refor- mulation of the Huber metric and
carefully chosen prior distributions. Our proposed method is invariant to
2-dimensional affine transforma- tions and, because it is relatively easy to
train and use, it is suited for general object matching problems.
