ImProc: converted tabs to spaces and fixed some typo's.

improc/ass3/report/report.tex

@@ -146,8 +146,8 @@ So, using the HSV color model does improve the results.
 
 \subsection{Finding Waldo}
 
-The assignment is to find Waldo (\emph{waldo.png}) in a large image containing
-Waldo and many other characters (\emph{waldo\_env.png}) using Histogram Backprojection.
+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.
 
 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:
@@ -156,28 +156,29 @@ Given histograms $M$ (model, Waldo) and $I$ (environment), create the back proje
 $b$ in the following steps:
 
 \begin{enumerate}
-	\item for each histogram bin $j$ do $R_j := frac{M_j}{I_j}$
-	\item for each $x, y$ do $b_{x,y} := min(R_{h(c_{x,y})}, 1)$
-	\item $b := D^r * b$
-	\item $(x_t, y_t) := loc(max_{x,y}, b_{x,y})$
+    \item for each histogram bin $j$ do $R_j := frac{M_j}{I_j}$
+    \item for each $x, y$ do $b_{x,y} := min(R_{h(c_{x,y})}, 1)$
+    \item $b := D^r * b$
+    \item $(x_t, y_t) := loc(max_{x,y}, b_{x,y})$
 \end{enumerate}
 
 However, the assignment tells us to only implement steps 1-3.
 
 \subsection{Mask}
 
-The algorithm is implemented in \emph{back\_projection.py}. First, a mask is created
-to ignore the white background in the \emph{waldo.png}. This is needed because the
-white color is not part of Waldo himself,. In fact, Waldo in the \emph{waldo\_env.png}
-has a yellowish background behind him. The usage of a mask is simple: if the mask value
-of a pixel is \texttt{False}, the pixel's color is discarded in the creation of the
-color histogram. The mask for Waldo is created by discarding all pixels with RGB
-color (255, 255, 255), which has the following result:
-
-\begin{figure}[h]
-	\label{fig:mask}
-	\includegraphics{mask.png}
-	\caption{The mask used to ignore the white background in \emph{waldo.png}.}
+The algorithm is implemented in \emph{back\_projection.py}. First, a mask is
+created to ignore the white background in the \emph{waldo.tiff}. This is needed
+because the white color is not part of Waldo himself. In fact, Waldo in the
+\emph{waldo\_env.tiff} has a yellowish background behind him. The usage of a
+mask is simple: if the mask value of a pixel is \texttt{False}, the pixel's
+color is discarded in the creation of the color histogram. The mask for Waldo
+is created by discarding all pixels with RGB color (255, 255, 255), which has
+the following result:
+
+\begin{figure}[H]
+    \label{fig:mask}
+    \includegraphics{mask.png}
+    \caption{The mask used to ignore the white background in \emph{waldo.tiff}.}
 \end{figure}
 
 \subsection{Basic algorithm}
@@ -192,11 +193,11 @@ weight mask.
 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.png}: Back projection of Waldo in the larger image, the
-	         red spot is Waldo's location.}
+\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.}
 \end{figure}
 
 This result is created in roughly 27 seconds on a laptop from 2009.
@@ -217,11 +218,11 @@ that are further away from all other estimators than the diagonal of the model i
 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.}
+\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.}
 \end{figure}
 
 \end{document}