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Fix to skimage.filter.rank.windowed_histogram docstring.

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Better explanation of technique in plot_windowed_histogram example, along with (hopefully correct) citations.
Relevant additions to release_dev.txt and CONTRIBUTORS.txt.
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Geoffrey French
2014-09-01 23:41:03 +01:00
parent bf02a92ee3
commit e6bda5accd
4 changed files with 31 additions and 11 deletions
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doc/examples/plot_windowed_histogram.py
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@@ -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