为了使检测的图象边界更符合有效的理想边界结构，同时能滤除边界图象中的噪声干扰，提出了一种基于遗传算法在图象边界规整方法。该方法首先将已经检测得到的边界图象编码为两维二值码串个体，并根据理想边界模板集来计算每个个体的适应度；然后通过交叉、变异和选择等遗传运算对被检测出的非理想边界进行规整。在遗传算法收敛时，该算法不仅能得到最适合有效理想边界结构的边界图象，并能有效地滤除边界图象中的噪声。

Many techniques in pattern recognition, robot vision, segmentation, feature extraction and etc require edge detection as a basic instrument. Although many methods have been suggested, the performance is quite different for different types of images and there is still not a general method. In this paper, we proposed a novel edge processing approach which makes the detected edge maps more valid and more ideal, instead of introducing a new edge detection method. The proposed method uses genetic algorithm to optimize the edge maps after edge detection. First, it encodes the edge maps into a two|dimensional binary array and determines the fitness based on valid edge structural templates for each individual. Second, the parent population is generated by changing a small part of pixels in edge maps randomly. Then the proposed method re|allocates edge points according to the genetic operators such as crossover and mutation, and forms their offspring population. Finally, elitist section is adopted to drive the genetic procedure approaching convergent state. When the genetic algorithm is converged, the optimized edge maps can be obtained and the noises in edge maps can be effectively reduced. The proposed method has been carried out for both the artificial and natural images, and the experimental results have shown its good performance.