Affine Invariant, Model-Based Object Recognition Using Robust Metrics and Bayesian Statistics

Details Affine Invariant, Model-Based Object Recognition Using Robust Metrics and Bayesian Statistics Affine Invariant, Model-Based Object Recognition Using Robust Metrics and Bayesian Statistics

2010-12-11

arxiv.org/abs/1012.2491v1

Image Analysis and Recognition Lecture Notes in Computer Science, 2005, Volume 3656/2005, 407-414

Computer Science

Computer Vision and Pattern Recognition

Scientific paper

10.1007/11559573_51

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.

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