X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/book_gpu.git/blobdiff_plain/0d39f3bfb1736ae41805f75a779e0bb01f4f5139..a2aa3f0f91a668ee6e799bad0f4de90b7b2be452:/BookGPU/Chapters/chapter3/ch3.tex

diff --git a/BookGPU/Chapters/chapter3/ch3.tex b/BookGPU/Chapters/chapter3/ch3.tex
index 1cf40c7..b8ff22c 100755
--- a/BookGPU/Chapters/chapter3/ch3.tex
+++ b/BookGPU/Chapters/chapter3/ch3.tex
@@ -4,7 +4,7 @@
 \newcommand{\kr}{\includegraphics[scale=0.6]{Chapters/chapter3/img/kernRight.png}}
 
 
-\chapter{Setting up the environnement.}
+\chapter{Setting up the environment.}
 Image processing using a GPU often means using it as a general purpose computing processor, which soon brings up the issue of data transfers, especially when kernel runtime is fast and/or when large data sets are processed.
 The truth is that, in certain cases, data transfers between GPU and CPU are slower than the actual computation on GPU. 
 It remains that global runtime can still be faster than similar processes run on CPU.
@@ -93,7 +93,7 @@ Median filtering is a well-known method used in a wide range of application fram
 First introduced by Tukey in \cite{tukey77}, it has been widely studied since then, and many researchers have proposed efficient implementations of it, adapted to various hypotheses, architectures and processors. 
 Originally, its main drawbacks were its compute complexity, its nonlinearity and its data-dependent runtime. Several researchers have addressed these issues and designed, for example, efficient histogram-based median filters with predictible runtimes \cite{Huang:1981:TDS:539567, Weiss:2006:FMB:1179352.1141918}.  
 
-More recently, the advent of GPUs opened new perspectives in terms of image processing performance, and some researchers managed to take advantage of the new graphics capabilities: in that respect, we can cite the Branchless Vectorized Median (BVM) filter \cite{5402362, chen09} which allows very interesting runtimes on CUDA-enabled devices but, as far as we know, the fastest implementation to date is the histogram-based PCMF median filter \cite{Sanchez-2-2012}.
+More recently, the advent of GPUs opened new perspectives in terms of image processing performance, and some researchers managed to take advantage of the new graphics capabilities: in that respect, we can cite the Branchless Vectorized Median (BVM) filter \cite{5402362, chen09} which allows very interesting runtimes on CUDA-enabled devices but, as far as we know, the fastest implementation to date is the histogram-based Parallel Ccdf-based Median Filter (PCMF) \cite{Sanchez-2-2012} where Ccdf means Complementary Cumulative Distribution Function.
 
 Some of the following implementations feature very fast runtimes. They are targeted on NVIDIA Tesla GPU (Fermi architecture, compute capability 2.x) but may easily be adapted to other models, e.g., those of compute capability 1.3.
 
@@ -256,7 +256,7 @@ Listing \ref{lst:medianForget1pix3} details this process where forgetful selecti
 \begin{figure}[b]
    \centering
    \includegraphics[width=6cm]{Chapters/chapter3/img/forgetful_selection.png}
-   \caption{Forgetful selection with the minimal element register count. Illustration for $3\times 3$ pixel window represented in a row and supposed sorted.}
+   \caption{Forgetful selection with the minimal element register count. Illustration for $3\times 3$ pixel window represented in a row and supposedly sorted.}
    \label{fig:forgetful_selection}
 \end{figure}
 \begin{figure}