Learning refinements on curve-strokes

摘要

We present a system to beautify curves: i.e. to take curves that roughly depict some property of interest and make them look more like what experts would draw. The focus of our work is in applications for artistic drawings, but our system can also be applied to various other domains where curves and trajectories play a dominant role such as robot path planning, animation or edge-deblurring. Our approach consists of learning properties from a database of ideal example, which could be comic sketches or robot trajectories, and transform a coarse input curve to make it look like those in the database. The key scientific issue is: in what sense are these curves 'like' one another? In our work, this likeness is expressed statistically. Using hidden Markov models in combination with multi-scale methods and mixture models, we synthesize a new curve as a statistically consistent mixture of the training set that best describes the input. Additionally, our approach also allows us to easily include predefined application specific models that can further bias the system.