25 March 2003 Using neural network to improve the performance of the hybrid evolutionary algorithm in image registration
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Abstract
The hybrid evolutionary algorithm is used for image registration formulated as an optimization problem of finding a vector of parameters minimizing the difference between images. The reproduction phase of the algorithm is enhanced with a two-level operation of local correction performed on the best genes in the reproduction pool. Random search is performed in the neighborhood of a gene until the time interval reaches a pre-set threshold. If the gene still retains its position in the pool, a refined multi-step search is performed using the Downhill simplex method. In order to improve the computational performance of the local search, local response analysis is used in the following way. All domains of the given reference image are classified according to their local response to a unit variation of the parameter vector. The classification scheme is based on a self-organizing neural network. During the local correction of the reproduction pool, the step size in the Downhill simplex search is modified according to the class of the image domain.
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Igor V. Maslov, Igor V. Maslov, Izidor Gertner, Izidor Gertner, } "Using neural network to improve the performance of the hybrid evolutionary algorithm in image registration", Proc. SPIE 5015, Applications of Artificial Neural Networks in Image Processing VIII, (25 March 2003); doi: 10.1117/12.477412; https://doi.org/10.1117/12.477412
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