Editing Scale-invariant feature transform (section)

=== Outlier detection ===
[[Outlier]]s can now be removed by checking for agreement between each image feature and the model, given the parameter solution. Given the [[linear least squares (mathematics)|linear least squares]] solution, each match is required to agree within half the error range that was used for the parameters in the [[Hough transform]] bins. As outliers are discarded, the linear least squares solution is re-solved with the remaining points, and the process iterated. If fewer than 3 points remain after discarding [[outlier]]s, then the match is rejected. In addition, a top-down matching phase is used to add any further matches that agree with the projected model position, which may have been missed from the [[Hough transform]] bin due to the similarity transform approximation or other errors.

The final decision to accept or reject a model hypothesis is based on a detailed probabilistic model.<ref name="Lowe2001" /> This method first computes the expected number of false matches to the model pose, given the projected size of the model, the number of features within the region, and the accuracy of the fit. A [[Bayesian probability]] analysis then gives the probability that the object is present based on the actual number of matching features found. A model is accepted if the final probability for a correct interpretation is greater than 0.98. Lowe's SIFT based object recognition gives excellent results except under wide illumination variations and under non-rigid transformations.