NoiseChisel underwent to large changes. Mainly as a result of the
previous addition to ImageCrop where an arbitary polygon could be
cropped. So I had to find a way to treat large blank areas in a the
image.

  1. The S/N value was previously found on each large mesh separately
     and would be interpolated and smoothed! On each mesh there
     usually isn't enough area to provide the right number of
     points. Unlike the sky and its standard deviation (noise) which
     can vary over the image, the Signal to noise ratio will only
     change when the data have different noise properties, for example
     more or less correlated noise. This can happen in an image,
     especially in surveys but it doesn't have to be on the same grid,
     so there will be problems.

     Now that ImageCrop can crop out an arbitary polygon, we can crop
     out the parts that have similar noise properties and work on
     them. Over the image the number statistics will be much better
     (for example in estimating the quantile). So NoiseChisel will now
     calculate the detection and segmentation Signal to noise ratios
     over the full image, not within one mesh.

  2. Since I wanted to work on a random polygon a large area of the
     image would be blank. Until now, I had not really put too much
     emphasis on blank pixels, but since their number significantly
     increased I had to find a solution. Now NoiseChisel's functions
     also account for unsigned char and long type blank values too.