Abstract: In order to compensate broken edges produced by traditional edge detectors, an effective Edge Linking method is proposed based on Fuzzy theory and Ant Colony Optimization algorithm（EL-FACO）. The method analyzes the endpoints of all the line segments from the edge image obtained by traditional detection approaches; according to the gradient information of each pixel within clique, it calculates the membership matrix based on fuzzy logic; the transition function is determined by gray level variation, membership and pheromone of each pixel, thus reducing the blindness of ant colony optimization and improving the accuracy of indexing edge points. The experimental results show the method can efficiently link disjointed edges and the compensating edges reflect the original edge information more accurately.

Key words: image processing, edge detection, broken edges linking, fuzzy logic, Ant Colony Optimization（ACO） algorithm

摘要： 针对边缘检测的断点问题，提出一种基于模糊理论和蚁群机制的断点连接方法。以原图像和传统检测算法得到的边缘为基础，分析出边缘端点，根据端点邻域内各像素的梯度信息，采用模糊判决方法，计算隶属度矩阵；由各像素的灰度梯度、隶属度和信息素确定转移函数，减小蚁群寻优的盲目性，提高边缘点定位的准确性。实验结果表明，该方法不仅能有效改善边缘不连续现象，且补偿边缘能更真实地反映原图像边缘信息。

关键词: 图像处理, 边缘检测, 断点连接, 模糊判决, 蚁群算法