Article: noisy image detection added

Article/method.tex

@@ -270,24 +270,28 @@ This distinction is illustrated on \RefFig{fig:edgeDir}.
   \label{fig:edgeDir}
 \end{figure}
 
-Another option, called multi-detection allows the detection of all the
-segments crossed by the input stroke $AB$.
-The multi-detection algorithm (Algorithm 1) is displayed below.
+\subsection{Multiple blurred segments detection}
 
-\input{Fig_method/algoMulti}
+Another option, called {\it multi-detection} (Algorithm 1), allows the
+detection of all the segments crossed by the input stroke $AB$.
+In order to avoid multiple detections of the same edge, an occupancy mask,
+initially empty, collects the dilated points of all the blurred segments,
+so that these points can not be add to another segment.
 
-First the positions $M_j$ of the local maxima of the gradient magnitude found
-under the stroke are sorted from the highest to the lowest.
+First the positions $M_j$ of the prominent local maxima of the gradient
+magnitude found under the stroke are sorted from the highest to the lowest.
 For each of them the main detection process is run with three modifications:
-i) the initial detection takes $M_j$ and the orthogonal direction $AB_\perp$
+\begin{enumerate}
+\item the initial detection takes $M_j$ and the orthogonal direction $AB_\perp$
 to the stroke as input to build a static scan of fixed width
 $\varepsilon_{ini}$, and $M_j$ is used as start point of the blurred segment;
-ii) an occupancy mask, initially empty, is filled in with the points of the
-detected blurred segments $\mathcal{B}_j''$ at the end of each successful
-detection;
-iii) points marked as occupied are rejected when selecting candidates for the
+\item the occupancy mask is filled in with the points of the detected blurred
+segments $\mathcal{B}_j''$ at the end of each successful detection;
+\item points marked as occupied are rejected when selecting candidates for the
 blurred segment extension in the fine tracking step.
-Multiple detections of the same edge are thus avoided.
+\end{enumerate}
+
+\input{Fig_method/algoMulti}
 
 In edge selection mode (\RefFig{fig:edgeDir} b), the multi-detection
 algorithm is executed twice, first in main edge selection mode, then
@@ -353,31 +357,49 @@ to collect all the segments found under the stroke.
 
 \input{Fig_method/algoAuto}
 
-The behavior of the unsupervised detection is depicted through the two
-examples of \RefFig{fig:auto}.
-The example on the left shows the detection of thin straight objects on a
-circle with variable width.
-On the left half of the circumference, the distance between both edges
-exceeds the initial assigned width and a thick blurred segment is build
-for each of them. Of course, on a curve, a continuous thickenning is
-observed untill the blurred segment minimal width reaches the initial
-assigned width.
-On the right half, both edges are encompassed in a common blurred segment,
-and at the extreme right part of the circle, the few distant residual points
-are grouped to form a thick segment.
-
-The example on the right shows the limits of the edge detector on a picture
-with quite dense repartition of gradient.
-All the salient edges are well detected but they are surrounded by a lot
-of false detections, that rely on the presence of many local maxima of
-the gradient magnitude with similar orientations.
+The performance of the detector is illustrated in \RefFig{fig:evalAuto}b
+or in \RefFig{fig:noisy} where hardly perceptible edges are detected in this
+quite textured image. When the initial value of the assigned width is small,
+short edges are detected edges. Longer edges are detected if the initial
+assigned width is larger, but the found segments incorporate a lot of
+interfering outliers.
 
 \begin{figure}[h]
 \center
-  \begin{tabular}{c@{\hspace{0.2cm}}c}
-    \includegraphics[width=0.37\textwidth]{Fig_method/vcercleAuto.png} &
-    \includegraphics[width=0.58\textwidth]{Fig_method/plafondAuto.png}
+  \begin{tabular}{c@{\hspace{0.2cm}}c@{\hspace{0.2cm}}c}
+    \includegraphics[width=0.32\textwidth]{Fig_method/parpaings.png} &
+    \includegraphics[width=0.32\textwidth]{Fig_method/parpaings2.png} &
+    \includegraphics[width=0.32\textwidth]{Fig_method/parpaings3.png}
   \end{tabular}
-  \caption{Automatic detection of blurred segments.}
-  \label{fig:auto}
+  \caption{Automatic detection of blurred segments on a quite texture image.}
+  \label{fig:noisy}
 \end{figure}
+
+%The behavior of the unsupervised detection is depicted through the two
+%examples of \RefFig{fig:auto}.
+%The example on the left shows the detection of thin straight objects on a
+%circle with variable width.
+%On the left half of the circumference, the distance between both edges
+%exceeds the initial assigned width and a thick blurred segment is build
+%for each of them. Of course, on a curve, a continuous thickenning is
+%observed untill the blurred segment minimal width reaches the initial
+%assigned width.
+%On the right half, both edges are encompassed in a common blurred segment,
+%and at the extreme right part of the circle, the few distant residual points
+%are grouped to form a thick segment.
+%
+%The example on the right shows the limits of the edge detector on a picture
+%with quite dense repartition of gradient.
+%All the salient edges are well detected but they are surrounded by a lot
+%of false detections, that rely on the presence of many local maxima of
+%the gradient magnitude with similar orientations.
+%
+%\begin{figure}[h]
+%\center
+%  \begin{tabular}{c@{\hspace{0.2cm}}c}
+%    \includegraphics[width=0.37\textwidth]{Fig_method/vcercleAuto.png} &
+%    \includegraphics[width=0.58\textwidth]{Fig_method/plafondAuto.png}
+%  \end{tabular}
+%  \caption{Automatic detection of blurred segments.}
+%  \label{fig:auto}
+%\end{figure}