Relation with computer vision and machine learning

[Andreas Mueller]

Hi Wei.
> Thanks for your reply! I see your great work in scikits-learn and your 
> comments are quite useful.
>
Thanks :)
> Many will overlap, I think we can maintain a list. As a quick example 
> in the following:
There is no discussion that machine learning methods are helpful for 
vision problems and that vision problems
are an important application domain for machine learning tools.

The question is if there is something you would like in skimage that 
would require the use of something from sklearn.
I.e. what algorithm you want in skimage is only useful together with 
something from sklearn?
I don't think any of your examples are in this category. Which is well 
enough, since this means
it should be easy to keep the two things separate ;)

Btw, MRFs in vision are often not learned, so this is no ML, just 
optimization. And I would rather place
that in skimage, as it is quite image specific. When I talked about 
graph-cuts, that's what I meant.
Normalized cuts are of limited use in low level vision since they are 
very slow for superpixels.
I was rather thinking of Boykov-Kolmogorov push-relabel - which is my 
next project when I get
my superpixels done. (Stefan actually mentioned he'd like to have it :)

Abour RBMs: I am 
<http://www.ais.uni-bonn.de/deep_learning/papers/ESANN10_schulz.pdf> no 
<http://www.ais.uni-bonn.de/deep_learning/papers/IJCNN10_mueller.pdf> 
stranger 
<http://www.ais.uni-bonn.de/%7Eschulz/publications/papers/nips10ws_schulz_mueller_behnke.pdf> 
to them 
<http://www.ais.uni-bonn.de/%7Eschulz/publications/papers/neurocomp11_schulz_mueller_behnke.pdf> 
but I would rather not include them in sklearn and definitely not in 
skimage.

Cheers,
Andy

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