scikit-image/skimage/filter/rank

To do
-----

* add simple examples, adapt documentation on existing examples
* add/check existing doc
* adapting tests for each type of filter

General remarks
---------------

Basically these filters compute local histogram for each pixel. Histogram is
build using a moving window in order to limit redundant computation. The path
followed by the moving window is given hereunder

 ...-----------------------\
/--------------------------/
\-------------------------- ...

A comparison is proposed with cmorph.dilate algorithm to show how computation
costs evolve with respect to image size or structuring element size. This
implementation gives better results for large structuring elements.

A local histogram is update at each pixel by introducing pixel entering the
structuring element border and by removing those leaving it. The histogram size
is 8bit (256 bins) for 8 bit images and 2 to 12 bit (up to 4096 bins) for 16bit
image depending on the image maximum value. Image with pixels higher than 4095
raise a ValueError.

The filter is applied up to the image border, the neighboorhood used is adjusted
accordingly. The user may provide a mask image (same size as input image) where
non zero value are the part of the image participating the the histogram
computation. By default all the image is filtered.