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Title:
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A ROBUST METHOD FOR ESTIMATING PROJECTIVETRANSFORMATIONS USING GENETIC ALGORITHMS |
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Author(s):
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Ran Song , John Szymanski |
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ISBN:
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978-972-8924-39-3 |
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Editors:
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António Palma dos Reis, Katherine Blashki and Yingcai Xiao (series editors:Piet Kommers, Pedro Isaías and Nian-Shing Chen) |
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Year:
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2007 |
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Edition:
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Single |
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Keywords:
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Genetic Algorithms, Crossover, Mutation, Projective Transformation |
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Type:
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Short Paper |
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First Page:
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122 |
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Last Page:
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126 |
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Language:
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English |
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Cover:
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Full Contents:
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click to dowload 
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Paper Abstract:
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This paper presents a robust method which provides quantitative estimates of the projective transformations between two
successive overlapping images using genetic algorithms. In this method, roulette selection and total arithmetic crossover
are applied based on real number encoding. Then an adaptive mutation operator is used to preserve the best solutions.
The experimental results show that the normalized registration error of the final solution exhibits a significant
improvement over those obtained by direct search approaches to such problems. Also, in contrast to other popular
approaches such as the least-squares and Levenberg-Marquardt algorithms, this algorithm can escape from local extrema
and can potentially realize the global optimum. |
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