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Proceedings Paper

Using local response analysis to reduce the computational cost of image registration with the hybrid genetic algorithm
Author(s): Igor V. Maslov
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Paper Abstract

The hybrid genetic algorithm (HGA) is used to solve image registration problem formulated as an optimization problem of finding components of a parameter vector minimizing the least squared difference between images. Analysis of image local response helps reduce the computational cost of local search, and genetic operations of selection and recombination. Unit variations of the components of the parameter vector are applied to images subject to registration. Corresponding variations of the objective function in small localities form an image response matrix. The reproduction phase of the algorithm includes a two-phase operation of local search and correction performed on the set of the best chromosomes in the reproduction pool. The step size of the local search is modified according to the values of the response matrix in the localities where the search is performed, which reduces the averaged computational cost of the correction over all iterations. The crossover and mutation phases of the HGA are based on the comparison of the response matrices of the images. The operation of correlation is applied to the response matrices of the reference and the registered images. The result serves as the probability matrix reducing the entire search space to subspaces that most likely contain the optimal solution to the problem. The operations of selection and recombination are performed only on those subspaces. Computational experiments with 2D grayscale images show that in some cases the proposed approach can significantly reduce the computational cost of image registration with the hybrid genetic algorithm.

Paper Details

Date Published: 30 December 2003
PDF: 8 pages
Proc. SPIE 5200, Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation VI, (30 December 2003); doi: 10.1117/12.505258
Show Author Affiliations
Igor V. Maslov, CUNY/Graduate Ctr. (United States)


Published in SPIE Proceedings Vol. 5200:
Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation VI
Bruno Bosacchi; David B. Fogel; James C. Bezdek, Editor(s)

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