Multidimensional Alignment Using the Euclidean Distance Transform

Abstract

Abstract We present a methodology for alignment of multidimensional data sets that is based on the Euclidean distance transform and the Marquardt–Levenberg optimization algorithm. The proposed approach operates on pixel or voxel descriptions of objects to be matched and estimates the parameters of a space transformation for optimal alignment of objects. The computational cost of an algorithm developed with this method is estimated. The methodology is tested by developing an algorithm for rigid body transformation alignment of three-dimensional data sets. Tests with synthetic and real objects indicate that the method is accurate, reliable, and robust.