Abstract

We describe a new approach to feature-based object recognition, using maximum a posteriori (MAP) estimation under a Markov random field (MRF) model. The main advantage of this approach is that it allows explicit modeling of dependencies between individual features of an object model. For instance, it can capture the fact that unmatched features due to partial occlusion are generally spatially coherent rather than independent. Efficient computation of the MAP estimate in our framework can be accomplished by finding a minimum cut on an appropriately defined graph. A special case of our framework yields even more efficient method, that does not use graph cuts. We call this technique spatially coherent matching. Our framework can also be seen as providing a probabilistic understanding of Hausdorff matching. We present ROC curves from Monte Carlo experiments that illustrate the improvement of the new spatially coherent matching technique over Hausdorff matching. 1 Introduction In this pap...

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