An axiomatic model of non bayesian updating

16 Jun

The probability that a match is correct can be determined by taking the ratio of distance from the closest neighbor to the distance of the second closest.An entry in a hash table is created predicting the model location, orientation, and scale from the match hypothesis.The hash table is searched to identify all clusters of at least 3 entries in a bin, and the bins are sorted into decreasing order of size.Hough transform identifies clusters of features with a consistent interpretation by using each feature to vote for all object poses that are consistent with the feature.When clusters of features are found to vote for the same pose of an object, the probability of the interpretation being correct is much higher than for any single feature.

The determination of consistent clusters is performed rapidly by using an efficient hash table implementation of the generalized Hough transform.Each cluster of 3 or more features that agree on an object and its pose is then subject to further detailed model verification and subsequently outliers are discarded.Such points usually lie on high-contrast regions of the image, such as object edges.Another important characteristic of these features is that the relative positions between them in the original scene shouldn't change from one image to another.The scale-invariant feature transform (SIFT) is an algorithm in computer vision to detect and describe local features in images.