为了使检测的图象边缘结构定位好，并且产生连续的精细边缘，同时能滤除边缘图象中的噪声干扰，基于费用函数最小化方法，提出了一种自适应免疫遗传算法用于图象的边缘检测.为了保持群体中个体的多样性，同时加快算法的收敛速度，该算法中交叉、变异和免疫算子采用了自适应变化而非固定的概率，同时免疫算子采用了几何形式的退火选择方案.由于该算法能够有效地利用局部边缘结构的一些先验知识和特征信息制作成免疫疫苗，其局部搜索能力较经典的遗传算法有很大的提高.该方法用于灰度图象时产生了令人满意的检测效果，并对噪声有较好的抑制作用

Edge detection is an important task in computer vision. It is the front-end processing stage in object recognition and image understanding system. In order to make the detected edges to be well localized, continuous and thin, and robust to noise, this paper presents an adaptive immune genetic algorithm (AIGA)based on cost minimization technique for edge detection. The proposed AIGA recommends the use of adaptive probabilities of crossover, mutation and immune operation, and a geometric annealing schedule in immune operator to realize the twin goals of maintaining diversity in the population and sustaining the fast convergence rate in solving the complex problems such as edge detection. Furthermore, AIGA can effectively exploit some prior knowledge and information of the local edge structure in the edge image to make vaccines, which results in much better local search ability of AIGA than that of the canonical genetic algorithm. Experimental results on gray-scale images show the proposed algorithm perform very well in terms of quality of the final edge image, rate of convergence and robustness to noise.