Morandi Machine Learning
By leveraging the features of a game engine framework, the system can analyse both the rendered image on the screen and obtain information on the position, depth and volume of the objects in the three-dimensional digital environment. Finding the optimal arrangement of visual elements for a painting is a manual and time-consuming activity that the painter pursues by applying general principles of design and personal aesthetic intuition. The system uses a three-dimensional digital prototype of the objects to paint or render in a design and proposes a computational technique to assist the painter or designer in the selection of the most aesthetically pleasing composition for a painting job. In this article, we illustrate how our system uses an evolutionary algorithm (EA) and four artificial neural networks to automate and speed up the creative process of compositional choice. Finally, we compare the results of our system with those produced by a method already used in commercial applications.