Abstract
In this paper, a simulated algorithm-genetic (SA-GA) hybrid algorithm based on a Markov Random Field (MRF) model (MRF-SA-GA) is introduced for image de-noising and segmentation. In this algorithm, a population of potential solutions is maintained at every generation, and for each solution a fitness value is calculated with a fitness function, which is constructed based on the MRF potential function according to Metropolis algorithm and Bayesian rule. Two experiments are selected to verify the performance of the hybrid algorithm, and the preliminary results show that MRF-SA-GA outperforms SA and GA alone.
Supported by the grants from the 973 Project (#2003CB716100), NSFC (#90208003, #30525030, # 30500140).
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© 2006 Springer-Verlag Berlin Heidelberg
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Du, X., Li, Y., Chen, W., Zhang, Y., Yao, D. (2006). A Markov Random Field Based Hybrid Algorithm with Simulated Annealing and Genetic Algorithm for Image Segmentation. In: Jiao, L., Wang, L., Gao, Xb., Liu, J., Wu, F. (eds) Advances in Natural Computation. ICNC 2006. Lecture Notes in Computer Science, vol 4221. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11881070_95
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DOI: https://doi.org/10.1007/11881070_95
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45901-9
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