First complete draft

documentation/perception.tex

@@ -1,4 +1,4 @@
-\section{Ball detection}
+\section{Ball Detection}
 \label{p sec ball detection}
 
 The very first task that needed to be accomplished was to detect the ball,
@@ -6,13 +6,13 @@ which is uniformly red-colored and measures about 6 cm in diameter. We decided
 to use a popular algorithm based on color segmentation \cite{ball-detect}. The
 idea behind this algorithm is to find the biggest red area in the image and
 assume that this is the ball. First, the desired color needs to be defined as
-an interval of HSV (Hue-Saturation-Value) values. After that, the image itself
-needs to be transformed into HSV colorspace, so that the regions of interest
-can be extracted into a \textit{binary mask}. The contours of the regions can
-then be identified in a mask \cite{contours}, and the areas of the regions can
-be calculated using the routines from the OpenCV library. The center and the
-radius of the region with the largest area are then determined and are assumed
-to be the center and the radius of the ball.
+an interval of HSV (Hue-Saturation-Value) \cite{hsv} values. After that, the
+image itself needs to be transformed into HSV colorspace, so that the regions
+of interest can be extracted into a \textit{binary mask}. The contours of the
+regions can then be identified in a mask \cite{contours}, and the areas of the
+regions can be calculated using the routines from the OpenCV library. The
+center and the radius of the region with the largest area are then determined
+and are assumed to be the center and the radius of the ball.
 
 \begin{figure}[ht]
   \includegraphics[width=\textwidth]{\fig ball-detection}
@@ -24,10 +24,10 @@ It is sometimes recommended \cite{ball-detect} to eliminate the noise in the
 binary mask by applying a sequence of \textit{erosions} and \textit{dilations},
 but we found, that for the task of finding the \textit{biggest} area the noise
 doesn't present a problem, whereas performing erosions may completely delete
-the image of the ball, if it is relatively far from the robot and the camera
-resolution is low. For this reason it was decided not to process the binary
-mask with erosions and dilations, which allowed us to detect the ball even over
-long distances.
+the image of the ball from the mask, if it is relatively far from the robot and
+the camera resolution is low. For this reason it was decided not to process the
+binary mask with erosions and dilations, which allowed us to detect the ball
+even over long distances.
 
 The advantages of the presented algorithm are its speed and simplicity. The
 major downside is that the careful color calibration is required for the
@@ -35,11 +35,11 @@ algorithm to function properly. If the HSV interval of the targeted color is
 too narrow, then the algorithm might miss the ball; if the interval is too
 wide, then other big red-shaded objects in the camera image will be detected as
 the ball. A possible approach to alleviate these issues to a certain degree
-will be presented further in this chapter. To conclude, we found this algorithm
-to be robust enough for our purposes, if the sensible color calibration was
-provided.
+will be presented further in the section \ref{p sec field detect}. To
+conclude, we found this algorithm to be robust enough for our purposes, if the
+sensible color calibration was provided.
 
-\section{Goal detection}
+\section{Goal Detection}
 \label{p sec goal detect}
 
 The goal detection presented itself as a more difficult task. The color of the
@@ -51,9 +51,7 @@ propose the following heuristic algorithm.
 
 \begin{figure}[ht]
   \includegraphics[width=\textwidth]{\fig goal-detection}
-  \caption{Goal Detection. On the right binary mask with all found contours. On
-    the left the goal, and one contour that passed preselection but was
-    rejected during scoring.}
+  \caption{Goal detection}
   \label{p figure goal-detection}
 \end{figure}
 
@@ -77,13 +75,13 @@ stage ends here, and the remaining candidates are passed to the scoring
 function.
 
 The scoring function calculates, how different are the properties of the
-candidates are from the properties, an idealized goal contour is expected to
-have. The evaluation is happening based on two properties. The first property
-is based on the observation, that the area of the goal contour is much smaller
-than the area of its \textit{enclosing convex hull} \cite{convex-hull}. The
-second observation is that all points of the goal contour must lie close to the
-enclosing convex hull. The mathematical formulation can then look like the
-following:
+candidates are from the properties, that an idealized goal contour is expected
+to have. The evaluation is happening based on two properties. The first
+property is based on the observation, that the area of the goal contour is much
+smaller than the area of its \textit{enclosing convex hull} \cite{convex-hull}.
+The second observation is that all points of the goal contour must lie close to
+the enclosing convex hull. The mathematical formulation of a corresponding
+scoring function can then look like the following:
 
 \begin{equation*}
   S(c)=\frac{A(c)}{A(Hull(c))}+\displaystyle\sum_{x_i \in c}\min_{h \in Hull(c)
@@ -93,21 +91,25 @@ following:
 The contour, that minimizes the scoring function, while keeping its value under
 a certain threshold is considered the goal. If no contour scores below the
 threshold, then the algorithm assumes that no goal was found. An important note
-is that the algorithm in such a way, that the preselection and scoring are
-modular, which means that the current simple scoring function can later be
-replaced by a function with a better heuristic, or even by some function that
-employs machine learning models.
+is that the algorithm is designed in such a way, that the preselection and
+scoring are modular, which means that the current simple scoring function can
+later be replaced by a function with a better heuristic, or even by some
+function that employs machine learning models.
 
 Our tests have shown, that when the white color is calibrated correctly, the
 algorithm can detect the goal almost without mistakes, when the goal is present
-in the image. Most irrelevant candidates candidates are normally discarded in
-the preselection stage, and the scoring function improves the robustness
-further. The downside of this algorithm, is that in some cases the field lines
+in the image. Most irrelevant candidates are normally discarded in the
+preselection stage, and the scoring function improves the robustness further.
+Figure \ref{p figure goal-detection} demonstrates the algorithm in action. On
+the right is the binary mask with all found contours. On the left are the goal,
+and one contour that passed preselection but was rejected during scoring.
+
+One downside of this algorithm, is that in some cases the field lines
 might appear to have the same properties, that the goal contour is expected to
 have, therefore the field lines can be mistaken for the goal. We will describe,
 how we dealt with this problem, in the section \ref{p sec field detect}.
 
-\section{Field detection}
+\section{Field Detection}
 \label{p sec field detect}
 
 The algorithm for the field detection is very similar to the ball detection