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 makes me a little nervous because no sanitizing was done on input. If a user inputs a floating point image already defined on the range `[0, 1]`, wouldn't this be incorrect?

If it's possible for all of these variants to operate natively on floating point images, and it looks like it should be, I would recommend running the input through `skimage.img_as_float()` at the very beginning. Then the function is robust to various input types, and you don't have to worry about scaling the image or the features at all.