Approximating images by optimally arranging polygons: a heuristic study into computational art

Approximating images by optimally arranging polygons: a heuristic study into computational art

It is possible to approximate artistic images from a limited number of stacked semi-transparent coloured polygons. To match the target image as closely as possible, the locations of the vertices, the drawing order of the polygons and the RGBA colour values must be optimised for the entire set at once.

Because of the vast combinatorial space, the relatively simple constraints and the well-defined objective function, these optimisation problems appear to be well suited for nature-inspired optimisation algorithms. In this pioneering study, we start off with sets of randomised polygons and try to find optimal arrangements for several well-known paintings using three iterative optimisation algorithms: stochastic hillclimbing, simulated annealing and the plant propagation algorithm.

We discuss the performance of the algorithms, relate the found objective values to the polygonal invariants and supply a challenge to the community.

Speaker

Daan van den Berg, University of Amsterdam

How to attend

If not a member of the Department of Mathematical Sciences at the University of Essex, you can register your interest in attending the seminar and request the Zoom's meeting password by emailing Dr Osama Mahmoud )o.mahmoud@essex.ac.uk).