
The first test (\RefFig{fig:synth}) compares the performance of both
detectors on a set of 1000 synthesized images containing 10 randomly
placed input segments with random width between 1 and 4 pixels.
Altough this perfect world context with low gradient noise tends to soften
the old detector weaknesses, the results of \RefTab{tab:synth} show slightly
better width and angle measurements on long segments for the new detector.
The new detector generates more small segments that degrade the angle
estimations, but it produces a smaller amount of false detections and
succeeds in finding most of the input segments.
\begin{figure}[h]
%\center
  \begin{tabular}{
      c@{\hspace{0.1cm}}c@{\hspace{0.1cm}}c@{\hspace{0.1cm}}c@{\hspace{0.1cm}}c}
    \includegraphics[width=0.19\textwidth]{Fig_synth/statsExample.png} &
    \includegraphics[width=0.19\textwidth]{Fig_synth/statsoldPoints.png} &
    \includegraphics[width=0.19\textwidth]{Fig_synth/statsoldBounds.png} &
    \includegraphics[width=0.19\textwidth]{Fig_synth/statsnewPoints.png} &
    \includegraphics[width=0.19\textwidth]{Fig_synth/statsnewBounds.png}
    \begin{picture}(1,1)
      \put(-310,0){a)}
      \put(-240,0){b)}
      \put(-170,0){c)}
      \put(-100,0){d)}
      \put(-30,0){e)}
    \end{picture}
  \end{tabular}
  \caption{Evaluation on synthesized images:
           a) one of the test images,
           b) output blurred segments from the old detector and
           c) their enclosing digital segments,
           d) output blurred segments from the new detector and
           e) their enclosing digital segments.}
  \label{fig:synth}
\end{figure}
\begin{table}
\centering
\input{Fig_synth/statsTable}
\caption{Measured performance of both detectors on a set of synthesized images.
$S$ is the set of all the input segments,
$D$ the set of all the detected blurred segments.}
\label{tab:synth}
\end{table}
