An algorithm for a drawing and painting robot - any tips?

Question

Algorithm for a drawing and painting robot -

Hello

I want to write a piece of software which analyses an image, and then produces an image which captures what a human eye perceives in the original image, using a minimum of bezier path objects of varying of colour and opacity.

Unlike the recent twitter super compression contest (see: stackoverflow.com/questions/891643/twitter-image-encoding-challenge), my goal is not to create a replica which is faithful to the image, but instead to replicate the human experience of looking at the image.

As an example, if the original image shows a red balloon in the top left corner, and the reproduction has something that looks like a red balloon in the top left corner then I will have achieved my goal, even if the balloon in the reproduction is not quite in the same position and not quite the same size or colour.

When I say "as perceived by a human", I mean this in a very limited sense. i am not attempting to analyse the meaning of an image, I don't need to know what an image is of, i am only interested in the key visual features a human eye would notice, to the extent that this can be automated by an algorithm which has no capacity to conceptualise what it is actually observing.

Why this unusual criteria of human perception over photographic accuracy?

This software would be used to drive a drawing and painting robot, which will be collaborating with a human artist (see: video.google.com/videosearch?q=mr%20squiggle).

Rather than treating marks made by the human which are not photographically perfect as necessarily being mistakes, The algorithm should seek to incorporate what is already on the canvas into the final image.

So relative brightness, hue, saturation, size and position are much more important than being photographically identical to the original. The maintaining the topology of the features, block of colour, gradients, convex and concave curve will be more important the exact size shape and colour of those features

Still with me?

My problem is that I suffering a little from the "when you have a hammer everything looks like a nail" syndrome. To me it seems the way to do this is using a genetic algorithm with something like the comparison of wavelet transforms (see: grail.cs.washington.edu/projects/query/) used by retrievr (see: labs.systemone.at/retrievr/) to select fit solutions.

But the main reason I see this as the answer, is that these are these are the techniques I know, there are probably much more elegant solutions using techniques I don't now anything about.

It would be especially interesting to take into account the ways the human vision system analyses an image, so perhaps special attention needs to be paid to straight lines, and angles, high contrast borders and large blocks of similar colours.

Do you have any suggestions for things I should read on vision, image algorithms, genetic algorithms or similar projects?

Thank you

Mat

PS. Some of the spelling above may appear wrong to you and your spellcheck. It's just international spelling variations which may differ from the standard in your country: e.g. Australian standard: colour vs American standard: color

Answer 1

There is an model that can implemented as an algorithm to calculate a saliency map for an image, determining which parts of the image would get the most attention from a human.

The model is called itti koch model
You can find a startin paper here
And more resources and c++ sourcecode here