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Next: Comparing two feature vectors. Up: Differential Invariants for Previous: EXTRACTION OF POINTS OF

MATCHING.

 

After having characterized each feature vector, we have to compare them together. The best solution seems to be the Mahalanobis distance [14] involving the covariance matrix of the vector components. It takes into account the way the components change and the possible correlation between each other. Moreover, this distance follows the tex2html_wrap_inline1253 distribution and a thresholding can easily be performed in order to eliminate the worst matches. But the results strongly depend on the estimation of this covariance matrix, which is quite difficult to obtain with accuracy because of the high number of various samples that its estimation requires. We will present in the next sections our solution to compute the likeness of two vectors.





Philippe Montesinos
Wed Jun 2 18:06:30 MET DST 1999