Author: Hakan Ardo <[email protected]>
Branch: extradoc
Changeset: r4483:b6959c6a2ab2
Date: 2012-08-08 21:20 +0200
http://bitbucket.org/pypy/extradoc/changeset/b6959c6a2ab2/
Log: typos
diff --git a/talk/dls2012/paper.tex b/talk/dls2012/paper.tex
--- a/talk/dls2012/paper.tex
+++ b/talk/dls2012/paper.tex
@@ -1007,7 +1007,7 @@
hardcoded into the implementation making the benchmark consist of a single
loop too.
\item {\bf conv3x3}: two-dimensional convolution with kernel of fixed
size $3 \times 3$ using a custom class to represent two-dimensional
- arrays. It is implemented as a two nested loops that iterates over the
elements of the
+ arrays. It is implemented as two nested loops that iterates over the
elements of the
$n\times n$ output matrix ${\bf B} = \left(b_{i,j}\right)$ and calculates each
element from the input matrix
${\bf A} = \left(a_{i,j}\right)$ and a kernel ${\bf K} = \left(k_{i,j}\right)$
using $b_{i,j} = $
\begin{equation}
@@ -1027,7 +1027,7 @@
of the optimizations.
\item {\bf sobel}: a low-level video processing algorithm used to
locate edges in an image. It calculates the gradient magnitude
- using sobel derivatives. A Sobel x-derivative $D_x$ of a $n \times n$ image
${I}$ is formed
+ using sobel derivatives. A Sobel x-derivative, $D_x$, of a $n \times n$
image, ${I}$, is formed
by convolving ${I}$ with
\begin{equation}
{K} = \left(
@@ -1038,7 +1038,7 @@
\end{array}
\right) ,
\end{equation}
-and a Sobel y-derivative $D_y$ is formed convolving $I$ with $K^\top$. The
gradient magnitude is
+and a Sobel y-derivative, $D_y$, is formed convolving $I$ with $K^\top$. The
gradient magnitude is
then formed for each pixel independently by $\sqrt{D_x^2 + D_y^2}$. The two
convolutions and the pixelwise
magnitude calculation are combined in the implementation of this benchmark and
calculated in a single pass over
the input image. This single pass consists of two nested loops with a somewhat
larger amount of calculations
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