The Chinese University of Hong Kong and City University of Hong Kong, Hong Kong.
IEEE Trans Vis Comput Graph. 2010 Mar-Apr;16(2):287-97. doi: 10.1109/TVCG.2009.85.
We propose a novel reaction diffusion (RD) simulator to evolve image-resembling mazes. The evolved mazes faithfully preserve the salient interior structures in the source images. Since it is difficult to control the generation of desired patterns with traditional reaction diffusion, we develop our RD simulator on a different computational platform, cellular neural networks. Based on the proposed simulator, we can generate the mazes that exhibit both regular and organic appearance, with uniform and/or spatially varying passage spacing. Our simulator also provides high controllability of maze appearance. Users can directly and intuitively "paint" to modify the appearance of mazes in a spatially varying manner via a set of brushes. In addition, the evolutionary nature of our method naturally generates maze without any obvious seam even though the input image is a composite of multiple sources. The final maze is obtained by determining a solution path that follows the user-specified guiding curve. We validate our method by evolving several interesting mazes from different source images.
我们提出了一种新的反应扩散（RD）模拟器来演化图像相似的迷宫。演化出的迷宫忠实地保留了源图像中的显著内部结构。由于传统的反应扩散很难控制所需图案的生成，我们在不同的计算平台——细胞神经网络上开发了我们的 RD 模拟器。基于所提出的模拟器，我们可以生成具有规则和有机外观的迷宫，具有均匀和/或空间变化的通道间隔。我们的模拟器还提供了对迷宫外观的高度可控性。用户可以直接和直观地“绘制”来以空间变化的方式修改迷宫的外观，方法是使用一组画笔。此外，我们的方法的进化性质自然会生成没有任何明显拼接的迷宫，即使输入图像是多个源的组合。最终的迷宫是通过确定遵循用户指定的导向曲线的解路径来获得的。我们通过从不同的源图像演化出几个有趣的迷宫来验证我们的方法。
