Abstract
Phase field model has been well acknowledged as an important method for image segmentation. This paper discussed the problem of jacquard image segmentation by approaching the phase field paradigm from a numerical approximation perspective. For fuzzy theory provides flexible and efficient techniques for dealing with conflicting optimization probelms, a novel fuzzy optimization algorithm for numerical solving of the model was proposed. To achieve global minimum of the model, a multi-start fuzzy strategy which combined a local minimization procedure with genetic algorithm was enforced. As the local minimization procedure does not guarantee optimality of search process, several random starting points need to be generated and used as input into global search process. In order to construct powerful search procedure by guidance of global exploration, genetic algorithm was applied to scatter the set of quasi-local mimizers into global positions. Experimental results show that the proposed algorithm is feasible, and reaches obvious effects in terms of jacquard image segmentation.
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Feng, Z., Yin, J., Zhang, H., Dong, J. (2005). A Phase-Field Based Segmentation Algorithm for Jacquard Images Using Multi-start Fuzzy Optimization Strategy. In: Wang, L., Jin, Y. (eds) Fuzzy Systems and Knowledge Discovery. FSKD 2005. Lecture Notes in Computer Science(), vol 3614. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11540007_30
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DOI: https://doi.org/10.1007/11540007_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28331-7
Online ISBN: 978-3-540-31828-6
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