Art and Computer Algorithms

Computer algorithm recreates Van Gogh painting in one hour

German researchers find computers can use algorithm to copy famous painting styles, including Picasso’s

Hannah Jane Parkinson | September 2, 2015

Computers: they make everything so easy these days. They check our spelling for us and help with maths, and now machines have added painting to the list of things they are better than us at.
Researchers in Germany have found that an algorithm can accurately copy the painting style of artists as disparate as Pablo Picasso and Vincent Van Gogh – and in just 60 minutes.

Source: http://mashable.com/2015/08/29/computer-photos/

<more at http://www.theguardian.com/technology/2015/sep/02/computer-algorithm-recreates-van-gogh-painting-picasso; related links: http://celebcafe.org/computer-algorithm-recreates-van-gogh-painting-in-one-hour-8198/ (Computer algorithm recreates Van Gogh painting in one hour. September 2, 2015) and http://mashable.com/2015/08/29/computer-photos/ (New algorithm gives photos Picasso-style makeovers. August 30, 2015); further: http://arxiv.org/abs/1508.06576 (A Neural Algorithm of Artistic Style
Leon A. Gatys, Alexander S. Ecker, Matthias Bethge. arXiv:1508.06576 [cs.CV] [Abstract: In fine art, especially painting, humans have mastered the skill to create unique visual experiences through composing a complex interplay between the content and style of an image. Thus far the algorithmic basis of this process is unknown and there exists no artificial system with similar capabilities. However, in other key areas of visual perception such as object and face recognition near-human performance was recently demonstrated by a class of biologically inspired vision models called Deep Neural Networks. Here we introduce an artificial system based on a Deep Neural Network that creates artistic images of high perceptual quality. The system uses neural representations to separate and recombine content and style of arbitrary images, providing a neural algorithm for the creation of artistic images. Moreover, in light of the striking similarities between performance-optimised artificial neural networks and biological vision, our work offers a path forward to an algorithmic understanding of how humans create and perceive artistic imagery.])>