From e5b7971291d2fc3a3fa18e363fa72b799b336196 Mon Sep 17 00:00:00 2001
From: even <philippe.even@loria.fr>
Date: Mon, 10 Dec 2018 01:03:49 +0100
Subject: [PATCH] Article: conclusion
---
Article/conclusion.tex | 54 ++++++++++++++++++++++++++++--------------
Article/expe.tex | 6 ++---
2 files changed, 39 insertions(+), 21 deletions(-)
diff --git a/Article/conclusion.tex b/Article/conclusion.tex
index 484283f..174e347 100755
--- a/Article/conclusion.tex
+++ b/Article/conclusion.tex
@@ -1,24 +1,42 @@
\section{Conclusion and perspectives}
-Gains importants en efficacit\'e.
+In this paper we introduced a new edge detector based on a local analysis of
+the image gradient and on the use of blurred segments to vehiculate an
+estimation of the edge thickness.
+It relies on directional scans of the image around maximal values of the
+gradient magnitude, that have previously been presented in a former paper.
+Despite of good performances obtained compared to existing detection methods
+found in the literature, the former approach suffers of two major drawbacks.
+It does not estimate the edge thickness so that many outliers are inserted
+into the blurred segment and the provided estmation of the edge orientation
+is biased.
+Then the scan direction is derived from a bounded blurred segment, that
+inevitably restricts its value to a finite set, so that long edges may be
+not completely detected.
+We solved these limitations through two new concepts:
+first the adaptive directional scans continuously that adjust the scan strip
+to the detected blurred segment direction;
+then the control of the assigned width based on the observation of the
+blurred segment thickenning in the early stage of its expansion.
-Tentative d'estimation de la largeur du segment, qui fiabilise
-l'estimation de l'orientation (plus de segments en travers).
+Expected gains in execution time linked to the suppression of a useless
+repetition of the fine tracking stage were confirmed by the experimental
+campaign both in supervised and unsupervised contexts.
+The residual weakness is the high sensitivity to the initial conditions
+despite of the valuable enhancement brought by the duplication of the
+initial detection.
+Disturbing gradient perturbations in the early stage of the edge expansion,
+possibly due to the presence of close edges, can deeply affect the output
+blurred segment.
+In supervised context, the user can easily select a favourable area where
+the awaited edge is dominant.
+But this default remains quite sensible in unsupervised context.
-Scans directionnels adaptatifs : une solution au probl\`eme de
-la non pr\'edictibilit\'e de l'orientation.
-
-D\'efauts persistants :
-\begin{itemize}
-\item L'\'epaisseur trouv\'ee n'est pas certifi\'ee.
-\item Le r\'esultat d\'epend des conditions initiales.
-Ca reste une m\'ethode instable, m\^eme si la duplication de la premi\`ere
-\'etape a permis de gagner en stabilit\'e.
-\item On n'est pas \`a l'abri d'un contour voisin qui vient perturber
-la d\'etection initiale ou l'affinement.
-Les filtres en fin de tracking sont l\`a pour soigner, pas pour gu\'erir.
-\end{itemize}
-
-Perspectives : validation sur contextes applicatifs.
+In future works, we intend to provide some protection against this drawback
+by scoring the detection result on the base of a characterization of the
+initial context.
+Then experimental validation of the consistency of the estimated
+width and orientation values on real situations are planned in
+different application fields.
%\section*{Acknowledgements}
diff --git a/Article/expe.tex b/Article/expe.tex
index 7dcc3ad..b48551b 100755
--- a/Article/expe.tex
+++ b/Article/expe.tex
@@ -68,8 +68,8 @@ detectors with the input strokes of \RefFig{fig:buro}.}
In the second series of tests, we compare the execution times of both detectors
for the automatic detection of edges on a set of test images. We display the
-results for one of them (\RefFig{fig:auto}). X (resp. Y) blurred segments are
-extracted with the former (resp. new) detector on all images. The
+results for one of them (\RefFig{fig:evalAuto}). X (resp. Y) blurred segments
+are extracted with the former (resp. new) detector on all images. The
average execution time is X ms for the former detector, and Y ms for the
new detector.
@@ -81,7 +81,7 @@ new detector.
\end{tabular}
\caption{Automatic edge detections on one of the test images with the
former detector on the left, and the new detector on the right.}
- \label{fig:auto}
+ \label{fig:evalAuto}
\end{figure}
The former detector do not estimate the edge width, but just circumscribes
--
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