From c060aa3123018c7a42c9bf9981a427d75cfad60f Mon Sep 17 00:00:00 2001
From: OTB Bot <otbbot@orfeo-toolbox.org>
Date: Sun, 29 Jan 2012 19:55:53 +0100
Subject: [PATCH] STYLE
---
Examples/DimensionReduction/ICAExample.cxx | 2 +-
Examples/DimensionReduction/NAPCAExample.cxx | 4 ++--
2 files changed, 3 insertions(+), 3 deletions(-)
diff --git a/Examples/DimensionReduction/ICAExample.cxx b/Examples/DimensionReduction/ICAExample.cxx
index 7aab2fe9e6..c8b77c1310 100644
--- a/Examples/DimensionReduction/ICAExample.cxx
+++ b/Examples/DimensionReduction/ICAExample.cxx
@@ -38,7 +38,7 @@
// orthogonal linear combinations, but the criterion of Fast ICA is
// different: instead of maximizing variance, it tries to maximize
// stastistical independance between components.
-//
+//
// In the Fast ICA algorithm \cite{hyvarinen1999fast},
// statistical independance is mesured by evaluating non-Gaussianity
// of the components, and the maximization is done in an iterative way.
diff --git a/Examples/DimensionReduction/NAPCAExample.cxx b/Examples/DimensionReduction/NAPCAExample.cxx
index ea61405997..629c023b8d 100644
--- a/Examples/DimensionReduction/NAPCAExample.cxx
+++ b/Examples/DimensionReduction/NAPCAExample.cxx
@@ -35,8 +35,8 @@
// efficient method based on the inner product in order to compute the
// covariance matrix.
//
-// The Noise-Adjusted Principal Component Analysis transform is a sequence
-// of two Principal Component Analysis transforms. The first transform is based
+// The Noise-Adjusted Principal Component Analysis transform is a sequence
+// of two Principal Component Analysis transforms. The first transform is based
// on an estimated covariance matrix of the noise, and intends to whiten the
// input image (noise with unit variance and no correlation between
// bands).
--
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