Article: figures 4 and 5 fusionned

Article/method.tex

@@ -53,10 +53,11 @@ The fine tracking step consists on building and extending a blurred segment
 $\mathcal{B}'$ based on points that correspond to local maxima of the
 image gradient, ranked by magnitude order, and with gradient direction
 close to a reference gradient direction at the segment first point.
-At this refinement step, the control of the assigned width is applied
+At this refinement step, a control of the assigned width is applied
 and an adaptive directional scanner based on the found position $C$ and
 direction $\vec{D}$ is used in order to extends the segment in the
-appropriate direction.
+appropriate direction. These two improvements are described in the
+following sections.
 
 The fine track output segment is finally filtered to remove artifacts
 and outliers, and a final blurred segment $\mathcal{B}''$ is provided.
@@ -67,34 +68,14 @@ The blurred segment is searched within a directional scan with a position
 and an orientation approximately provided by the user, or blindly defined
 in unsupervised mode.
 Most of the time, the detection stops where the segment escapes sideways
-from the scan strip (\RefFig{fig:escape}).
+from the scan strip (\RefFig{fig:escape} a).
 A second search is then run using another directional scan aligned
-on the detected segment.
+on the detected segment (\RefFig{fig:escape} b).
 However, even in case of a correct detection, the estimated orientation
 of the segment is subject to the numerization rounding,
 and the longer the real segment to detect, the higher the probability to
 fail again on a blurred segment escape from the directional scan.
 
-\begin{figure}[h]
-\center
-  \begin{tabular}{c@{\hspace{0.2cm}}c@{\hspace{0.2cm}}c}
-    \includegraphics[width=0.31\textwidth]{Fig_notions/escapeFirst_zoom.png} &
-    \includegraphics[width=0.31\textwidth]{Fig_notions/escapeSecond_zoom.png} &
-    \includegraphics[width=0.31\textwidth]{Fig_notions/escapeThird_zoom.png}
-  \end{tabular}
-  \begin{picture}(1,1)(0,0)
-    \put(-314,-12){a)}
-    \put(-200,-12){b)}
-    \put(-86,-12){c)}
-  \end{picture}
-  \caption{Aborted detections on side escapes from the directional scan
-           during the initial tracking step (a) and during the fine tracking
-           step (b), and complete detection using an adaptive directional
-           scan (c). The input selection is drawn in red color, the scan
-           strip bounds in blue and the detected blurred segment in green.}
-  \label{fig:escape}
-\end{figure}
-
 %Even in ideal situation where the detected segment is a perfect line,
 %its width is never null as a result of the discretization process.
 %The estimated direction accuracy is mostly constrained by the length of
@@ -155,23 +136,55 @@ width $\varepsilon$ ($k$ is a constant arbitrarily set to 4).
 The last clause expresses the update of the scan bounds at iteration $i$.
 Compared to static directional scans, the scan strip moves while
 scan lines remain fixed.
-An example of adaptive directional scan is given in \RefFig{fig:adaption}.
+This behavior ensures a complete detection of the blurred segment even
+when the orientation is badly estimated (\RefFig{fig:escape} c).
 
 \begin{figure}[h]
 \center
   \begin{tabular}{c@{\hspace{0.2cm}}c}
-    \includegraphics[width=0.49\textwidth]{Fig_notions/adaptionBounds_zoom.png}
-    & \includegraphics[width=0.49\textwidth]{Fig_notions/adaptionLines_zoom.png}
+    \includegraphics[width=0.48\textwidth]{Fig_notions/escapeFirst_zoom.png} &
+    \includegraphics[width=0.48\textwidth]{Fig_notions/escapeSecond_zoom.png} \\
+    \multicolumn{2}{c}{
+    \includegraphics[width=0.98\textwidth]{Fig_notions/escapeThird_zoom.png}}
+    \begin{picture}(1,1)(0,0)
+      {\color{dwhite}{
+        \put(-260,134.5){\circle*{8}}
+        \put(-86,134.5){\circle*{8}}
+        \put(-172,7.5){\circle*{8}}
+      }}
+      \put(-263,132){a}
+      \put(-89,132){b}
+      \put(-175,5){c}
+    \end{picture}
   \end{tabular}
-  \caption{Example of blurred segment detection
-           using an adaptive directional scan.
-           On the right picture, the scan bounds are displayed in red, the
-           detected blurred segment in blue, and its bounding lines in green.
-           The left picture displays the successive scans.
-           Here the adaption is visible at the crossing of the tile joins.}
-  \label{fig:adaption}
+  \caption{Aborted detections on side escapes of static directional scans
+           and successful detection using an adaptive directional scan.
+           The last points added to the left of the blurred segment during
+           the initial detection (a) lead to a bad estimation of its
+           orientation, and thus to an incomplete fine detection with
+           a classical directional scanner (b). This scanner is
+           advantageously replaced by an adaptive directional scanner
+           able to continue the segment expansion as far as necessary (c).
+           The input selection is drawn in red color, the scan strip bounds
+           in blue and the detected blurred segment in green.}
+  \label{fig:escape}
 \end{figure}
 
+%\begin{figure}[h]
+%\center
+%  \begin{tabular}{c@{\hspace{0.2cm}}c}
+%    \includegraphics[width=0.49\textwidth]{Fig_notions/adaptionBounds_zoom.png}
+%    & \includegraphics[width=0.49\textwidth]{Fig_notions/adaptionLines_zoom.png}
+%  \end{tabular}
+%  \caption{Example of blurred segment detection
+%           using an adaptive directional scan.
+%           On the right picture, the scan bounds are displayed in red, the
+%           detected blurred segment in blue, and its bounding lines in green.
+%           The left picture displays the successive scans.
+%           Here the adaption is visible at the crossing of the tile joins.}
+%  \label{fig:adaption}
+%\end{figure}
+
 \subsection{Control of the assigned width}
 
 The assigned width $\varepsilon$ to the blurred segment recognition algorithm