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Merge pull request #2115 from noahstier/master

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Move skimage.filters.rank description and todos from README into docstring.
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Egor Panfilov
2016-05-30 10:45:05 +03:00
parent de0d7a2ae1 2232061402
commit 292a0bee3c
2 changed files with 34 additions and 34 deletions
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skimage/filters/rank/README.rst
-32
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@@ -1,32 +0,0 @@
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. A histogram is
built using a moving window in order to limit redundant computation. The path
followed by the moving window is given hereunder
...-----------------------\
/--------------------------/
\-------------------------- ...
We compare grey.dilate to this histogram based method to show how
computational costs increase with respect to image size or structuring element
size. This implementation gives better results for large structuring elements.
The local histogram is updated at each pixel as the structuring element window
moves by, i.e. only those pixels entering and leaving the structuring element
update the local histogram. The histogram size is 8-bit (256 bins) for 8-bit
images and 2 to 16-bit for 16-bit images depending on the maximum value of the
image.
The filter is applied up to the image border, the neighborhood used is
adjusted accordingly. The user may provide a mask image (same size as input
image) where non zero values are the part of the image participating in the
histogram computation. By default the entire image is filtered.
skimage/filters/rank/generic.py
+34 -2
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@@ -1,5 +1,29 @@
"""The local histogram is computed using a sliding window similar to the method
described in [1]_.
"""
General Description
-------------------
These filters compute the local histogram at each pixel, using a sliding window
similar to the method described in [1]_. A histogram is built using a moving
window in order to limit redundant computation. The moving window follows a
snake-like path:
...------------------------\
/--------------------------/
\--------------------------...
The local histogram is updated at each pixel as the structuring element window
moves by, i.e. only those pixels entering and leaving the structuring element
update the local histogram. The histogram size is 8-bit (256 bins) for 8-bit
images and 2- to 16-bit for 16-bit images depending on the maximum value of the
image.
The filter is applied up to the image border, the neighborhood used is
adjusted accordingly. The user may provide a mask image (same size as input
image) where non zero values are the part of the image participating in the
histogram computation. By default the entire image is filtered.
This implementation outperforms grey.dilation for large structuring elements.
Input image can be 8-bit or 16-bit, for 16-bit input images, the number of
histogram bins is determined from the maximum value present in the image.
@@ -7,6 +31,14 @@ histogram bins is determined from the maximum value present in the image.
Result image is 8-/16-bit or double with respect to the input image and the
rank filter operation.
To do
-----
* add simple examples, adapt documentation on existing examples
* add/check existing doc
* adapting tests for each type of filter
References
----------