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

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 reformulation of the Huber metric and carefully chosen prior distributions. Our proposed method is invariant to 2-dimensional affine transformations and, because it is relatively easy to train and use, it is suited for general object matching problems.

• Type: Journal
• Year: 2010
• Where: CORR
• Authors: Vasileios Zografos, Bernard F. Buxton
• Keywords: 2-dimensional Affine Transformations, CORR 2010, Education, Model-based Object Recognition, Non-robust Error Norm