[eu-gene] need advice on a framework design

[Bager Akbay]

Dear All,

I am writing an MA thesis about a generative art framework at
Interface Culture department, Linz Art University.
This is a long email, I tried but could not make it shorter.

I have simply separated the framework into 3 parts.

Analyzers : To analyze images
Generators : To create images
Minds : To understand what is analyzed and to guide what is created.

Analyzers take an image as an input and gives a number as an output.
The system will have many analyzers. Analyzers might vary from
brightness analysis to face recognition.

Minds are the part in which framework gets useful. Artists will be
expected to design minds and see other people's designed ones.
Minds will ask generators to create some images, and ask analyzers to
analyze them. Then they will force!! (evolutionary or parametric)
generators to create more desired ones.

I have a problem with generators. (in fact most of them are filters,
not really generators)
Generators are basic filters like photoshop filters.

There is a logical error I made here, and I can't solve it.
Filters does not create images,  so that I don't have any images to start with.
I skipped that step in the beginning and used random photos I have in
a folder, and built the system.

Now what I need is a random image.
What is a random image ?
How can we really create a random image ?

When we go like
for every pixel {
r: random(0,255)
g: random(0,255)
b: random(0,255)
}

That is pretty random and covers whole image space but mostly! does
not make sense for any human. We cant compare or recall any of them.
When we say get last uploaded flickr image, it might be random for
uploaded photos but does not cover the whole image space.

So that I started to think about something like "humanoid image space"
but then that sounded stupid because even an rgb noise is humanoid, we
even have a name for it.
Than I changed it to humanly distributed image space.

I need an algorithm which covers all image space but it probably
produce humanoid images with a right distribution.

With more clear words ;
A system creates 100 random images, none of them looks like each other
(for a human not mathematically). They are almost evenly distributed.
They are all recognizable.

Is not that really hard ?

--
bager