Abstract
Point pattern matching is a fundamental problem in computer vision and pattern recognition. Membrane computing is an emergent branch of bio-inspired computing, which provides a novel idea to solve computationally hard problems. In this paper, a new point pattern matching algorithm with local elitism strategy is proposed based on membrane computing models. Local elitism strategy is used to keep good correspondences of point pattern matching found during the search, so the matching rate and the convergence speed are improved. Five heuristic mutation rules are introduced to avoid the local optimum. Experiment results on both synthetic data and real world data illustrate that the proposed algorithm is of higher matching rate and better stability.
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Acknowledgments
This work was supported by the National Natural Science Foundation of China (61073116, 61272152 and 60903105), Scientific Research Foundation for Doctor of Anhui University (02203104), Natural Science Foundation of Anhui Higher Education Institutions of China (J2012A010, KJ2012A008).
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Ding, Z., Tang, J., Zhang, X., Luo, B. (2013). A Local Elitism Based Membrane Evolutionary Algorithm for Point Pattern Matching. In: Yin, Z., Pan, L., Fang, X. (eds) Proceedings of The Eighth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA), 2013. Advances in Intelligent Systems and Computing, vol 212. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37502-6_103
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DOI: https://doi.org/10.1007/978-3-642-37502-6_103
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