Optimization of Image Edge Maps with Genetic Algorithm
图象边界的遗传算法规整

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.