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.
@article{arxiv.1012.2491,
title = {Affine Invariant, Model-Based Object Recognition Using Robust Metrics and Bayesian Statistics},
author = {Vasileios Zografos and Bernard Buxton},
journal= {arXiv preprint arXiv:1012.2491},
year = {2010}
}