基于膜计算模型的点集匹配算法

点集匹配是计算机视觉和模式识别领域中的一个经典NP问题。膜计算为自然计算的新分支，旨在从单个细胞或组织及器官等细胞群的结构和功能中抽象出新的计算模型或计算思想。在嵌套结构膜优化算法的基础上，提出了一种新的基于膜计算模型的点集匹配算法，结合点集匹配问题的特点，算法引入了三种新的启发式搜索规则，在一定程度上进一步提高了匹配的正确率。与传统优化算法相比，这种新的方法具有更好的全局搜索能力，因此，能够获得点集匹配问题的较好解。实验结果表明，该方法对点集匹配问题的求解是有效的，具有较高的匹配精度和较好的稳定性。

Membrane computing model based algorithm for point set matching

Point set matching is one of the classical NP problems in computer vision and pattern recognition. Membrane computing is an emergent branch of natural computing, which aims to abstract innovative computing models or computing ideas from the structure and function of a single cell or from complexes of cells, such as tissues and organs. On the basis of membrane optimization algorithms with hierarchical structure and the feature of the point set matching problem, a novel point set matching algorithm was proposed. In this algorithm, three new heuristic search rules were introduced, by which matching rate increased to some extent. Compared to the traditional optimization algorithms, the algorithm exhibited a better global search capability, thus a better solution for point set matching problem was obtained. Experimental results illustrate that the proposed algorithm is effective on both matching rate and stability.