From 3b67ffebbd8e5074bfd2e2f05530109705d9be7b Mon Sep 17 00:00:00 2001
From: Guillaume Pasero <guillaume.pasero@c-s.fr>
Date: Fri, 30 Sep 2016 17:20:58 +0200
Subject: [PATCH] DOC: fix indentation for rst
---
Documentation/Cookbook/rst/recipes/pbclassif.rst | 7 ++++---
1 file changed, 4 insertions(+), 3 deletions(-)
diff --git a/Documentation/Cookbook/rst/recipes/pbclassif.rst b/Documentation/Cookbook/rst/recipes/pbclassif.rst
index 0f5a7ce801..a8e79cc7a0 100644
--- a/Documentation/Cookbook/rst/recipes/pbclassif.rst
+++ b/Documentation/Cookbook/rst/recipes/pbclassif.rst
@@ -104,10 +104,10 @@ There are several strategies to compute those sampling rates:
of samples, which is user-defined.
* **Smallest class strategy:** The class with the least number of samples
will be fully sampled. All other classes will be sampled with the
-same number of samples.
+ same number of samples.
* **Percent strategy:** Each class will be sampled with a user-defined
percentage (same value for all classes) of samples available in this
-class.
+ class.
* **Total strategy:** A global number of samples to generate is
divided proportionally among each class (classes proportions are
enforced).
@@ -269,7 +269,7 @@ image.
- *Mode = equal:* For each image :math:`i` and each class :math:`c`,
:math:`N_i( c ) = (total / L) * (\frac{Ti(c)}{sum_k(Ti(k))})` where :math:`total` is the total number of samples specified
- *Mode = custom:* For each image :math:`i` and each class :math:`c`,
- :math:`Ni( c ) = total(i) * (\frac{Ti(c)}{sum_k(Ti(k))})` where :math:`total(i)` is the total number of samples specified for image :math:`i`
+ :math:`Ni( c ) = total(i) * (\frac{Ti(c)}{sum_k(Ti(k))})` where :math:`total(i)` is the total number of samples specified for image :math:`i`
* **Strategy = smallest class**
@@ -722,6 +722,7 @@ used to predict output values. The applications to do that are and .
.. figure:: ../Art/MonteverdiImages/classification_chain_inputimage.jpg
.. figure:: ../Art/MonteverdiImages/classification_chain_fancyclassif_CMR_input.png
.. figure:: ../Art/MonteverdiImages/classification_chain_fancyclassif_CMR_3.png
+
Figure 6: From left to right: Original image, fancy colored classified image and regularized classification map with radius equal to 3 pixels.
The input data set for training must have the following structure :
--
GitLab