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We propose a new approach to global optimization algorithm based on controlled randomsearch techniques for rotational alignment of spherical surfaces with associated scalar values. To reducethe distortion in correspondence and increase efficiency, the spherical surface is first re-sampled usinga geodesic sphere. The rotation in space is represented using the modified Rodrigues parameters.Correspondence between two spherical surfaces is implemented in the parametric domain. We appliedthe methods to the alignment of beam patterns computed from the outer ear shapes of bats. Theproposed method is compared with other approaches such as alignment based on principal componentanalysis (PCA), exhaustive search in the discrete space of rotations defined by Euler angles and directsearch using uniform samples over the rotation group SO(3). Experimental results demonstrate that theproposed rotation alignment obtained using the proposed algorithm has a high degree of precision andgive the best result among the other four approaches

Globally optimal rotation alignment of spherical surfaces with associated scalar values