Improving local search with neural network in image registration with the hybrid evolutionary algorithm

Image registration is formulated as a nonlinear optimization problem of finding-an affine transformation minimizing the difference between images. A particular scheme of the hybrid evolutionary algorithm is used to solve the problem. The reproduction phase of the algorithm is enhanced with a two-phase operation of local search and correction performed on the subset of the best chromosomes in the reproduction pool. In order to reduce the computational cost of the correction, a mechanism of the adaptive control of the local search is designed, based on a micro model of the local image response. The mechanism correlates the step size of the search with the local properties of the gray level surface at different points of an image. To reduce the number of evaluations of the local image response required by the control mechanism all participating image points are evaluated and classified at once in the preprocessing stage. A self-organizing neural network is employed to classify different points according to their response values, and to build an adaptive, compact response map of the entire image. During the execution of the main algorithm, this map is used as a lookup table, to retrieve the appropriate response values for the points participating in the local search.