Phillip C. Reiner

Aperiodic Gradient represents the proof of concept of a generic research. The question was how to create a continuous gradient by culling non-repetitive, non-random patterns. In order to determine possible point locations a large point cloud was created. With the help of machine learning tools the cloud is subdivided based on relative proximities of each point. This results in a series of sub-clouds roughly equal in size. A desired curve function is mapped as an attribute of intensity to each set. Depending on the distance to an attractor each points intensity is remap onto the predefined range of its containing sub-cloud. A threshold of this value will determine the boolean of being culled.