Automatic generation of aesthetic patterns with cloud model
Tao Wu, Limin Zhang, Junjie Yang · 2016
Computational aesthetics is in the ascendant and widely applied in various fields, whose uncertainty mechanism is still a challenge. The definition of cloud model is extended in the paper, an automatic generation algorithm of aesthetic patterns with cloud model is proposed, in which two types of cloud model is involved. The key technique is the selection of seed points and the rendering of repeated circles. The influence of different parameters on the aesthetic qualities of pattern images is also investigated, including types of circles, parent-child size ratio, the number of obstacles, filling opacity and color coding. The experiment results suggest that the proposed method is with advantages of uncertainty, simplicity and efficiency, and would inspire graphic designers who may be interested in subtle aesthetic patterns created automatically.