ImProc: Added machine specs and reformatted paragraphs.

improc/ass3/report/report.tex

@@ -147,13 +147,15 @@ So, using the HSV color model does improve the results.
 \subsection{Finding Waldo}
 
 The assignment is to find Waldo (\emph{waldo.tiff}) in a large image containing
-Waldo and many other characters (\emph{waldo\_env.tiff}) using Histogram Backprojection.
+Waldo and many other characters (\emph{waldo\_env.tiff}) using Histogram
+Backprojection.
 
-The idea of Histogram Backprojection is explained in the paper by Swain and Ballard,
-so we will not explain it here. The algorithm as described in the paper is as follows:
+The idea of Histogram Backprojection is explained in the paper by Swain and
+Ballard, so we will not explain it here. The algorithm as described in the
+paper is as follows:
 
-Given histograms $M$ (model, Waldo) and $I$ (environment), create the back projection
-$b$ in the following steps:
+Given histograms $M$ (model, Waldo) and $I$ (environment), create the back
+projection $b$ in the following steps:
 
 \begin{enumerate}
     \item for each histogram bin $j$ do $R_j := frac{M_j}{I_j}$
@@ -183,46 +185,49 @@ the following result:
 
 \subsection{Basic algorithm}
 
-The algorithm is implemented in the function \texttt{hbp}:
-First, $M$ and $I$ are created using \texttt{colHist}. $R$ is then created by
-division (step 1). Afterwards, the back projection is created using the loop in step 2.
-Finally, a convolution function is used to soften the back projection. To create the
-convolution, we have used the function \texttt{scipy.ndimage.correlate} with a circular
-weight mask.
+The algorithm is implemented in the function \texttt{hbp}: First, $M$ and $I$
+are created using \texttt{colHist}. $R$ is then created by division (step 1).
+Afterwards, the back projection is created using the loop in step 2.  Finally,
+a convolution function is used to soften the back projection. To create the
+convolution, we have used the function \texttt{scipy.ndimage.correlate} with a
+circular weight mask.
 
-The following result is generated with 64 bins in each color dimension and a convolution
-radius of 15 pixels:
+The following result is generated with 64 bins in each color dimension and a
+convolution radius of 15 pixels:
 
 \begin{figure}[H]
     \hspace{-4cm}
     \includegraphics[width=20cm]{found_waldo.png}
-    \caption{\emph{found\_waldo.tiff}: Back projection of Waldo in the larger image, the
-             red spot is Waldo's location.}
+    \caption{\emph{found\_waldo.tiff}: Back projection of Waldo in the larger
+    image, the red spot is Waldo's location.}
 \end{figure}
 
-This result is created in roughly 27 seconds on a laptop from 2009.
+This result is created in roughly 27 seconds on a laptop from 2009, with an
+Intel dual core and 4 GB RAM.
 
 \subsection{Experimental cluster detection using K-means}
 
-In addition to the assignment, we thought it would be interesting to be able to draw a
-rectangle around possible occurences of Waldo in the image. Therefore, we created the
-function \texttt{find\_peaks}, which returns a number of possible locations averages.
-To do this, we eliminate all pixels in the back projection which are below a certain
-threshold (which is given as e number from 0 to 1, and then multiplied by the maximum
-value in the projection). This leaves us with a number of pixels clusters, of which we
-can calculate the centers using K-means
-algorithm\footnote{\url{http://en.wikipedia.org/wiki/K-means\_clustering}} (function
-\texttt{scipy.cluster.vq.kmeans}). To find the initial estimators, we find all pixels
-that are further away from all other estimators than the diagonal of the model image
-(\emph{waldo.png}). Around each center pixel, a rectangle with the size of the model
-image is drawn over the larger image. With 32 bins in each color dimension, a threshold
-of 0.25 and a convolution radius of 10 pixels, the result is as follows:
+In addition to the assignment, we thought it would be interesting to be able to
+draw a rectangle around possible occurences of Waldo in the image. Therefore,
+we created the function \texttt{find\_peaks}, which returns a number of
+possible locations averages.  To do this, we eliminate all pixels in the back
+projection which are below a certain threshold (which is given as e number from
+0 to 1, and then multiplied by the maximum value in the projection). This
+leaves us with a number of pixels clusters, of which we can calculate the
+centers using K-means
+algorithm\footnote{\url{http://en.wikipedia.org/wiki/K-means\_clustering}}
+(function \texttt{scipy.cluster.vq.kmeans}). To find the initial estimators, we
+find all pixels that are further away from all other estimators than the
+diagonal of the model image (\emph{waldo.png}). Around each center pixel, a
+rectangle with the size of the model image is drawn over the larger image. With
+32 bins in each color dimension, a threshold of 0.25 and a convolution radius
+of 10 pixels, the result is as follows:
 
 \begin{figure}[H]
     \hspace{-4cm}
     \includegraphics[width=20cm]{k-means.png}
-    \caption{\emph{k-means.png}: Multiple possible locations using a low threshold
-             and convolution radius.}
+    \caption{\emph{k-means.png}: Multiple possible locations using a low
+    threshold and convolution radius.}
 \end{figure}
 
 \end{document}