Fix to skimage.filter.rank.windowed_histogram docstring.

Better explanation of technique in plot_windowed_histogram example, along with (hopefully correct) citations.
Relevant additions to release_dev.txt and CONTRIBUTORS.txt.

CONTRIBUTORS.txt

@@ -185,3 +185,6 @@
 
 - Adam Feuer
   PIL Image import and export improvements
+
+- Geoffrey French
+  skimage.filters.rank.windowed_histogram and plot_windowed_histogram example.

doc/examples/plot_windowed_histogram.py

@@ -4,22 +4,38 @@ from __future__ import division
 Sliding window histogram
 ========================
 
-This example extracts a single coin from the `skimage.data.coins` image and
-generates a histogram of its greyscale values.
+Histogram matching can be used for object detection in images [1]_.
+This example extracts a single coin from the `skimage.data.coins` image
+and uses histogram matching to attempt to locate it within the original
+image.
 
-It then computes a sliding window histogram of the complete image using
-`skimage.filter.rank.windowed_histogram`. The local histogram for the region
-surrounding each pixel in the image is compared to that of the single coin,
-with a similarity measure being computed and displayed.
+First, a box-shaped region of the image containing the target coin is
+extracted and a histogram of its greyscale values is computed.
+
+Next, for each pixel in the test image, a histogram of the greyscale values
+in a region of the image surrounding the pixel is computed.
+`skimage.filter.rank.windowed_histogram` is used for this task, as it
+employs an efficient sliding window based algorithm that is able to compute
+these histograms quickly [2]_.
+The local histogram for the region surrounding each pixel in the image is
+compared to that of the single coin, with a similarity measure being
+computed and displayed.
 
 The histogram of the single coin is computed using `numpy.histogram` on a
 box shaped region surrounding the coin, while the sliding window histograms
 are computed using a disc shaped structural element of a slightly different
 size. This is done in aid of demonstrating that the technique still finds
-similarity inspite of these differences.
+similarity in spite of these differences.
 
 To demonstrate the rotational invariance of the technique, the same
 test is performed on a version of the coins image rotated by 45 degrees.
+
+References
+----------
+.. [1] Porikli, F. "Integral Histogram: A Fast Way to Extract Histograms
+       in Cartesian Spaces" CVPR, 2005. Vol. 1. IEEE, 2005
+.. [2] S.Perreault and P.Hebert. Median filtering in constant time.
+       Trans. Image Processing, 16(9):2389-2394, 2007.
 """
 import numpy as np
 import matplotlib

doc/release/release_dev.txt

@@ -20,6 +20,7 @@ Region Adjacency Graphs
  - Similarity RAGs (#1080)
  - Normalized Cut on RAGs (#1080)
  - RAG Drawing (#1087)
+Sliding Windowed Histogram (#1127)
 
 Improvements
 ------------

skimage/filter/rank/generic.py

@@ -895,10 +895,10 @@ def windowed_histogram(image, selem, out=None, mask=None, shift_x=False, shift_y
     out : 3-D array with float dtype of dimensions (H,W,N), where (H,W) are
         the dimensions of the input image and N is n_bins or image.max()+1
         if no value is provided as a parameter. Effectively, each pixel
-        is an N-dimensional feature vector that is the histogram.
-        The sum of the elements in the feature vector will be 1, unless
-        no pixels in the window were covered by both selem and mask, in which
-        case all elements will be 0.
+        is a N-D feature vector that is the histogram. The sum of the
+        elements in the feature vector will be 1, unless no pixels in the
+        window were covered by both selem and mask, in which case all
+        elements will be 0.
 
     Examples
     --------