From d1b32265754ff37e6a65335d7b9c3d68e731faf4 Mon Sep 17 00:00:00 2001
From: even <philippe.even@loria.fr>
Date: Wed, 19 Dec 2018 16:38:09 +0100
Subject: [PATCH] Article: figures 4 and 5 fusionned
---
Article/method.tex | 81 +++++++++++++++++++++++++++-------------------
1 file changed, 47 insertions(+), 34 deletions(-)
diff --git a/Article/method.tex b/Article/method.tex
index e46a0d7..304df28 100755
--- a/Article/method.tex
+++ b/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
--
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