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English corrections in entropy example

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Olivier Debeir
2015-09-07 09:44:47 +02:00
parent 0fed219560
commit 86542c5916
1 changed files with 6 additions and 6 deletions
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doc/examples/plot_entropy.py
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@@ -7,16 +7,16 @@ In information theory, information entropy is the log-base-2 of the number of
possible outcomes for a message.
For an image, local entropy is related to the complexity contained in a given
neighborhood, typically defined by a structuring element. A large number of
various gray levels has a higher entropy than an homogeneous neighborhood.
neighborhood, typically defined by a structuring element.
The entropy filter can detect subtle variations of local gray level distribution.
The entropy filter can detect subtle variations in the local gray level
distribution.
In the example, the image is composed of two surfaces with two slightly
different distributions.
Image has a uniform random distribution in the range [-14, +14] in the middle of the
image and a uniform random distribution in the range [-15, 15] at
the image borders, both centered at a gray value of 128.
The image has a uniform random distribution in the range [-14, +14] in the
middle of the image and a uniform random distribution in the range [-15, 15]
at the image borders, both centered at a gray value of 128.
We apply the local entropy measure using a circular structuring element of
radius 10. As a result, one can detect the central square. The radius is