From 86542c5916cfa35bd7300b68d2befb2dbb384974 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Mon, 7 Sep 2015 09:44:47 +0200 Subject: [PATCH] English corrections in entropy example --- doc/examples/plot_entropy.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/doc/examples/plot_entropy.py b/doc/examples/plot_entropy.py index 8bcfdc52..29c70c20 100644 --- a/doc/examples/plot_entropy.py +++ b/doc/examples/plot_entropy.py @@ -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