diff --git a/Article/Fig_method/algoAuto.tex b/Article/Fig_method/algoAuto.tex
new file mode 100644
index 0000000000000000000000000000000000000000..bafd70f2331b2c20c37f546820469531e3c5de70
--- /dev/null
+++ b/Article/Fig_method/algoAuto.tex
@@ -0,0 +1,45 @@
+\begin{algorithm}[h]
+  \SetAlgoLined
+  \SetKwInOut{Input}{input}
+  \SetKwInOut{Output}{output}
+
+  \SetKwData{image}{$\mathcal{I}$}
+  \SetKwData{iwidth}{$W_I$}
+  \SetKwData{iheight}{$H_I$}
+  \SetKwData{resol}{$\delta$}
+  \SetKwData{nullset}{$\emptyset$}
+  \SetKwData{Result}{Result}
+
+  \SetKwArray{mask}{$\mathcal{M}$}
+  \SetKwArray{bslist}{BSL}
+  
+  \SetKwFunction{multi}{MultiDetect}
+  \SetKwFunction{pt}{Pt}
+
+  \SetKwData{Begin}{Start}
+  \SetKwData{End}{End}
+  
+  \Input{Image \image, width \iwidth, height \iheight, resolution \resol}
+  \Output{\textit{\bslist} $\rightarrow$ list of detected blurred segments}
+  \BlankLine
+  \bslist $\leftarrow$ \nullset\;
+  \mask $\leftarrow$ \nullset\;
+  $i \leftarrow$ \resol/2\;
+  \Repeat{$i <$ \iwidth/2}{
+    \bslist $\leftarrow$ \bslist + \multi (\pt(\iwidth/2-i,\iheight),
+                                           \pt(\iwidth/2-i,0), \mask)\;
+    \bslist $\leftarrow$ \bslist + \multi (\pt(\iwidth/2+i,\iheight),
+                                           \pt(\iwidth/2+i,0), \mask)\;
+    $i \leftarrow i -$ \resol\;
+  }
+  $i \leftarrow$ \resol/2\;
+  \Repeat{$i <$ \iheight/2}{
+    \bslist $\leftarrow$ \bslist + \multi (\pt(0,\iheight/2-i),
+                                           \pt(\iwidth,\iheight/2-i), \mask)\;
+    \bslist $\leftarrow$ \bslist + \multi (\pt(0,\iheight/2+i),
+                                           \pt(\iwidth,\iheight/2+i), \mask)\;
+    $i \leftarrow i -$ \resol\;
+  }
+  
+  \caption{AutoDetect: finds all blurred segments in the image.}
+\end{algorithm}
diff --git a/Article/Fig_method/plafondAuto.png b/Article/Fig_method/plafondAuto.png
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diff --git a/Article/Fig_method/plafond_petit.png b/Article/Fig_method/plafond_petit.png
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diff --git a/Article/Fig_method/vcercle.png b/Article/Fig_method/vcercle.png
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diff --git a/Article/Fig_method/vcercleAuto.png b/Article/Fig_method/vcercleAuto.png
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diff --git a/Article/main.tex b/Article/main.tex
index 6fa922eb18ff44477f54826cc3265819b98cf6de..d01ce795ae216addeb8cc80568db6eb67be514be 100755
--- a/Article/main.tex
+++ b/Article/main.tex
@@ -4,7 +4,7 @@
 %\usepackage[T1]{fontenc}
 
 \usepackage{graphicx}
-\graphicspath{{./images/}{./images/introduction}}
+%\graphicspath{{./Fignotions/}{./Figmethod}}
 
 \usepackage[ruled,vlined]{algorithm2e}
 
diff --git a/Article/method.tex b/Article/method.tex
index 63adc79987dea8f28c6cc62bb1fd97d46bada938..111ad61262ca6711bb7dd28741c224f52f7e46dd 100755
--- a/Article/method.tex
+++ b/Article/method.tex
@@ -95,7 +95,6 @@ iii) points marked as occupied are rejected when selecting candidates for the
 blurred selegment extension in the fine tracking step.
 
 \input{Fig_method/algoMulti}
-%\input{algo1}
 
 Beyond the possible detection of a large set of edges at once, the
 multi-detection allows the detection of some unaccessible edges in
@@ -162,9 +161,44 @@ This example illustrates our detector versatility.
 
 \subsection{Automatic blurred segment detection}
 
-Explication et b\'etail de l'algo
+An unsupervised mode is also proposed to automatically detect all the
+straight edges in the image. A stroke that crosses the whole image, is
+swept in both direction, vertical then horizontal, from the center to
+the borders. At each position, the multi-detection algorithm is run
+to collect all the segments found under the stroke.
+
+\input{Fig_method/algoAuto}
+
+The behaviour of the unsupervised detection is depicted through the two
+examples of \RefFigure{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 be a lot
+of false detections, that rely on the presence of many local maxima of
+the gradient magnitude with similar orientations.
 
-\subsection{Implementation details}
+\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}
 
-A directional scanner is encoded as an iterator that provides successively
-all the scan lines.
+% \subsection{Implementation details}
+%
+% A directional scanner is encoded as an iterator that provides successively
+% all the scan lines.