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documentation/Yuankai/bdetect.tex

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+\section{Ball Detection}
+\label{p sec ball detection}
+
+The very first task that needed to be accomplished was to detect the ball,
+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) \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}
+  \caption[Ball detection]{Ball detection. On the right is the binary mask}
+  \label{p figure ball-detection}
+\end{figure}
+
+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 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 a careful color calibration is required for the
+algorithm to function properly. If the HSV interval of the targeted color is
+too narrow, the algorithm might miss the ball; if the interval is too
+wide, 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 the section \ref{p sec field detect}. To
+conclude, we found this algorithm to be robust enough for our purposes, if a
+sensible color calibration was provided.