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.