Niblack and Sauvola Local thresholding.

Hello,
I implemented Niblack and Sauvola, two commonly used binarization techniques. They're implemented using Integral images to speedup computation (see http://www.csse.uwa.edu.au/~shafait/papers/Shafait-efficient-binarization-SPIE08.pdf). I put them in skimage.filter.thresholding.py.

The complete list of implemented algorithms is:
- Niblack. 
- Sauvola.
- Sauvola, Wolf's modification.
- Sauvola, Phansalkar's modification.

Thank you.
This step seems expensive, since `_mean_std` was computed above. Shouldn't the mean and std scale linearly with the image? So, we just need to divide `m` and `s` by 255 as well, rather than recompute from scratch. At any rate, it might be worthwhile reorganising the flow so that `_mean_std` is only called once, regardless of the method.