A Phase-Field Based Segmentation Algorithm for Jacquard Images Using Multi-start Fuzzy Optimization Strategy
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.
KeywordsImage Segmentation Phase Field Phase Field Model Fuzzy Optimization Phase Field Method
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- 2.Feng, Z.L., Yin, J.W., Chen, G., Dong, J.X.: Research on jacquard fabrics image denoising using Allen-Cahn level set model. Journal of Zhejiang University (Engineering Science) 39, 185–189 (2005)Google Scholar
- 12.Hagen, L.W., Kahng, A.B.: Combining problem reduction and adaptive multi-start: a new technique for superior iterative partitioning. IEEE Transaction on CAD 16, 709–717 (1997)Google Scholar
- 15.De Giorgi, E., Carriero, M., et al.: Existence theorem for a minimum problem with free discontinuity set. Archive for Rational Mechanics and Analysis 11, 291–322 (1990)Google Scholar
- 18.Fedrizzi, M., Karcprzyk, J., Verdagay, J.L.: A survey of fuzzy optimization and mathematical programming. In: Interactive Fuzzy Optimization. Lecture Notes in Economics and Mathematical Systems, pp. 15–28 (1991)Google Scholar
- 19.Kawarada, H., Suito, H.: Fuzzy Optimization Method, Computational Science for the 21st Century. John Wiley & Sons, Chichester (1997)Google Scholar