Image registration using entropic graph-matching criteria

Image registration requires the specification of a class of discriminatory image features and an appropriate image dissimilarity measure. Entropic spanning graphs produce a consistent estimator of feature entropy and divergence. Direct estimators with non-parametric "plug-in" density estimators, on single pixels and independent image component feature vectors are compared. A technique for minimum spanning tree construction with significantly lower memory and time complexity is investigated. On the basis of misregistration errors with decreasing SNR, the minimal graph entropy estimator can have better performance than indirect estimators. In general, misregistration errors are lower with higher dimensional ICA feature vectors as compared to single pixels.