Article: conclusion

Article/conclusion.tex

 \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}

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