preface

[book_gpu.git] / BookGPU / Chapters / chapter4 / ch4.tex

diff --git a/BookGPU/Chapters/chapter4/ch4.tex b/BookGPU/Chapters/chapter4/ch4.tex
index d618753ae9b6fe8a1cf4ab2797c536c8d4bbff1f..e3dbfd5c1fd87fef6ae39c74c9f8bf86b8964b10 100644 (file)
--- a/BookGPU/Chapters/chapter4/ch4.tex
+++ b/BookGPU/Chapters/chapter4/ch4.tex
@@ -1,4 +1,7 @@
-\chapterauthor{Gilles Perrot}{FEMTO-ST Institute}
+\chapterauthor{Gilles Perrot}{Femto-ST Institute, University of Franche-Comte, France}
+
+\chapter{Implementing an efficient convolution operation on GPU}
+
 
 %\newcommand{\kl}{\includegraphics[scale=0.6]{Chapters/chapter4/img/kernLeft.png}~}
 %\newcommand{\kr}{\includegraphics[scale=0.6]{Chapters/chapter4/img/kernRight.png}}
@@ -29,10 +32,10 @@
 %%   }
 
 
-\chapter{Implementing an efficient convolution \index{Convolution} operation on GPU}
+
 \section{Overview}
 In this chapter, after dealing with GPU median filter implementations,
-we propose to explore how convolutions can be implemented on modern
+we propose to explore how convolutions\index{Convolution}  can be implemented on modern
 GPUs. Widely used in digital image processing filters, the \emph{convolution
 operation} basically consists in taking the sum of products of elements
 from two 2-D functions, letting one of the two functions move over