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This paper proposes an improvement over the traditional SIFT-based object recognition methodology proposed by Lowe [3]. This improvement corresponds to a fast probabilistic model for hypothesis rejection (affine solution verification stage), which allows a large reduction in the number of false positives. The new probabilistic model is evaluated in an object recognition task using a database of 100 pairs of images.

A Fast Probabilistic Model for Hypothesis Rejection in SIFT-Based Object Recognition