混沌免疫模糊聚类算法在图像边缘检测中的应用

Application of a Novel Fuzzy Clustering Method Based on Chaos Immune Evolutionary Algorithm for Edge Detection in Image Processing

针对图像处理中的模糊边缘检测问题,提出一种混沌免疫模糊聚类算法.该算法把混沌变量加载于免疫算法的变量群体中,利用混沌搜索的特点对群体进行微小扰动并逐步调整扰动幅度,明显改善了免疫算法的群体多样性.实验结果表明,该算法不仅具有很强的模糊边缘和微细边缘检测能力,而且可以提高基于人工免疫进化算法的模糊聚类算法的搜索效率.

A novel fuzzy clustering method based on chaos immune evolutionary algorithm (CIEFCM) was proposed to solve fuzzy edge detection problems in image processing. In CIEFCM, a tiny disturbance was added to filial generation group by using chaos variable and the disturbance amplitude was adjusted step by step, which greatly improves the colony diversity of immune evolution algorithm (IEA). The experimental results show that the method can not only detect the fuzzy edge and exiguous edge correctly, but also improve the searching efficiency of fuzzy clustering algorithm based on IEA evidently.