摘要
分形算法是自然景物建模的一种有效技术,普通分形算法往往存在部分与整体严格自相似的问题,即使加入随机因素的扰动,仍不能逼真体现自然景物的遗传生长过程。提出了以自然树生成为例,吸收遗传算法的思想,对树枝特征进行编码,以树枝为单位对编码进行遗传操作,通过父代树枝的遗传操作生成子代树枝,使父代与子代之间保持相似又不严格相似,更贴切地模拟树木的遗传生长过程。实验结果表明,采用此算法生成的分形树随机性更强、效果更逼真。融入遗传操作,能有效弥补普通分形算法的严格自相似问题和随机函数在模拟自然景物遗传生长过程方面的不足,并可推广至其他分形递归算法。
Fractal is an effective technology in the modeling of nature scenery, but the strict self-similarity of the traditional fractal algorithms has limited their efficiency, Even when a random issue is corporated, the process of genetic growth is still not simulated appropriately. In this paper, the concept of genetic operation are adopted in the traditional fractal-tree generating algorithms, the characteristic of the branch is encoded, and the cross-over, mutation, selection are operated on the code, The characteristic of the child branch is generated from the genetic operation of the parent branch; they are similar but not identical. As a result, an image that is self-similar but not strictly self-similar can be generated and the process of genetic growth can be simulated more appropriately. The case study and simulation shows the fractal-trees generated by the new algorithm are more stochastic and more real. The genetic operation can effectively remedy the strictly self-similar of the fractal algorithm and the limitation of the random issue in the modeling of natural scenery, and this algorithm could be extended to other fractal recursive algorithms.
出处
《中国图象图形学报》
CSCD
北大核心
2008年第8期1560-1565,共6页
Journal of Image and Graphics
关键词
分形树
随机扰动
交配
变异
参数控制
随机数产生器
fractal-tree, random perturbation, cross over, mutation, parameter control, random number generator
