Article: multi-detection

Article/Fig_method/algoMulti.tex

+\begin{algorithm}[h]
+  \SetAlgoLined
+  \SetKwInOut{Input}{input}
+  \SetKwInOut{Output}{output}
+
+  \SetKwData{lm}{LocMax}
+  \SetKwData{nullset}{$\emptyset$}
+  \SetKwData{ortho}{$\vec{AB}_\perp$}
+  \SetKwData{eps}{$\varepsilon_{ini}$}
+  \SetKwData{pta}{$A$}
+  \SetKwData{ptb}{$B$}
+  \SetKwData{Result}{Result}
+
+  \SetKwArray{occmask}{OccupancyMask}
+  \SetKwArray{bseg}{BlurredSegment}
+  \SetKwArray{bslist}{ListOfBlurredSegments}
+  
+  \SetKwFunction{locmax}{ComputeAndSortGradientLocalMax}
+  \SetKwFunction{sortgradmax}{SortByGradientMagnitude}
+  \SetKwFunction{taille}{Size}
+  \SetKwFunction{cardinal}{Card}
+
+  \SetKwData{Begin}{Start}
+  \SetKwData{End}{End}
+  
+  \Input{Stroke points \pta, \ptb}
+  \Output{\textit{\bslist} $\rightarrow$ list of detected blurred segments}
+  \BlankLine
+  \bslist $\leftarrow$ \nullset\;
+  \occmask $\leftarrow$ \nullset\;
+  \lm $\leftarrow$ \locmax (\pta, \ptb)\;
+  \BlankLine
+  \For{$i \leftarrow 0$ \KwTo \taille(\lm)}{
+    \bseg $\leftarrow$ detect (\lm[i], \ortho, \eps, \occmask)\;
+    \For{$j \leftarrow 0$ \KwTo \cardinal (\bseg)}{
+      \occmask $\leftarrow$ \bseg[j]\;
+  }
+    \bslist $\leftarrow$ \bseg\;
+  }
+  
+  \caption{MultiDetection:
+           finds all blurred segments under an input stroke.}
+\end{algorithm}

Article/Fig_method/edgeDir.readme

+Images obtenues pas le test testEdgeDir.txt
+suivi de la commande de detection multiple : Ctrl-M
+avec deux modalites pilotees par Ctrl-E
+
+Sur gimp selection de la zone (34,166)(444,314).

Article/Fig_method/testEdgeDir.txt

+287 142
+326 103

Article/Fig_method/testVoisin.txt

+329 210
+323 245

Article/Fig_method/voisin.readme

+Images obtenues pas le test testvoisin.txt
+suivi de la commande de detection de bords : Ctrl-E
+
+Sur gimp selection de la zone (282,90)(411,156).

Article/method.tex

@@ -2,7 +2,7 @@
 
 \subsection{Workflow of the detection process}
 
-The blurred segment detector workflow is summerized
+The work-flow of the blurred segment detection process is summerized
 in the following figure.
 
 \begin{figure}[h]
@@ -12,8 +12,8 @@ in the following figure.
     \put(-2,18){\scriptsize $(A,B)$}
     \put(-2,15){\vector(1,0){24}}
     \put(24,0){\framebox(56,30)}
-    \put(24,15){\makebox(56,10){Initial}}
-    \put(24,3){\makebox(56,10){detection}}
+    \put(24,16){\makebox(56,10){Initial}}
+    \put(24,4){\makebox(56,10){detection}}
     \put(86,18){\scriptsize $\mathcal{B}_{1}$}
     \put(80,15){\vector(1,0){22}}
     %\put(102,0){\framebox(56,30)}
@@ -25,15 +25,15 @@ in the following figure.
     \put(159,18){\scriptsize $(C,\vec{D})$}
     \put(158,15){\vector(1,0){28}}
     \put(186,0){\framebox(56,30)}
-    \put(186,15){\makebox(56,10){Fine}}
-    \put(186,3){\makebox(60,10){tracking}}
+    \put(186,16){\makebox(56,10){Fine}}
+    \put(186,4){\makebox(60,10){tracking}}
     \put(250,18){\scriptsize $\mathcal{B}_2$}
     \put(242,15){\vector(1,0){24}}
     \put(266,0){\framebox(56,30){Filtering}}
     \put(330,18){\scriptsize $\mathcal{B}_3$}
     \put(322,15){\vector(1,0){22}}
   \end{picture}
-  \caption{The detection method work flow.}
+  \caption{The detection process main work-flow.}
   \label{fig:workflow}
 \end{figure}
 
@@ -63,21 +63,108 @@ Setting $N=20$ shows a good behaviour on tested images.
 The fine track output segment is finally filtered to remove artifacts
 and outliers, and a solution blurred segment $\mathcal{B}_3$ is provided.
 
-\subsection{The thinning process}
+\subsection{Supervised blurred segment detection}
+
+In supervised context, the user draws an input stroke across the specific
+edge he wants to extract from the image.
+The detection method previously described is continuously run during mouse
+dragging and the output blurred segment is displayed on-the-fly.
+
+The method is very sensitive to the local conditions of the initial detection
+so that the output blurred segment may be quite unstable.
+In order to temper this undesirable behaviour for particular applications,
+the initial detection can be optionally run twice, the second fast scan being
+aligned on the first detection output.
+This strategy provides a first quick analysis of the local context before
+extracting the segment and contributes to notably stabilize the overall
+process.
+
+Another option, called multi-detection allows the detection of all the
+segments crossed by the input stroke $AB$.
+The multi-detection algorithm is displayed below.
+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.
+For each of them the main detection process is run with three modifications:
+i) the initial detection takes $M_j$ and the orthogonal direction $AB_\perp$
+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) a occupancy mask, initially empty, is filled in with the points of the
+detected blurred segments $\mathcal{B}_{j3}$ at the end of each successful
+detection;
+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
+classical single detection mode. This is particularly the case of edges
+that are quite close to a more salient edge with a higher gradient.
+The multi-detection detects both edges and the user may then select
+the awaited one.
 
-\subsection{Multi-detection}
+\begin{figure}[h]
+\center
+  \begin{tabular}{c@{\hspace{0.2cm}}c@{\hspace{0.2cm}}c@{\hspace{0.2cm}}c}
+    \includegraphics[width=0.22\textwidth]{Fig_method/voisinImage_zoom.png} &
+    \includegraphics[width=0.22\textwidth]{Fig_method/voisinGrad_zoom.png} &
+    \includegraphics[width=0.22\textwidth]{Fig_method/voisinSingle_zoom.png} &
+    \includegraphics[width=0.22\textwidth]{Fig_method/voisinMulti_zoom.png} \\
+    \parbox{0.22\textwidth}{\centering{\scriptsize{a)}}} &
+    \parbox{0.22\textwidth}{\centering{\scriptsize{b)}}} &
+    \parbox{0.22\textwidth}{\centering{\scriptsize{c)}}} &
+    \parbox{0.22\textwidth}{\centering{\scriptsize{d)}}}
+  \end{tabular}
+  \caption{Example of edge, detected only in multi-detection mode:
+    a) the stroke on the intensity image,
+    b) the gradient map,
+    c) the result of the classical single mode detection,
+    d) the result of the multi-detection. }
+  \label{fig:voisins}
+\end{figure}
+
+This detection procedure can be used to dectect as well straight edges
+as thin straight objects. In the first case, the gradient vectors of all
+edge points are assumed to be oriented in the same direction. But if the
+sign of the gradient direction is not considered, points with gradient in
+opposite directions are merged to build the same blurred segment, allowing
+the detection of both edges of a thin linear structure, like for instance
+the tile joins of \RefFigure{fig:edgeDir}.
+
+\begin{figure}[h]
+\center
+  \begin{tabular}{c@{\hspace{0.2cm}}c}
+    \includegraphics[width=0.46\textwidth]{Fig_method/multiStroke_zoom.png} &
+    \includegraphics[width=0.46\textwidth]{Fig_method/multiEdge_zoom.png} \\
+    \parbox{0.46\textwidth}{\centering{\scriptsize{a)
+            Detection of straight lines}}} &
+    \parbox{0.46\textwidth}{\centering{\scriptsize{b)
+            Detection of straight edges}}}
+  \end{tabular}
+  \caption{Testing the gradient direction to detect as well edges as
+           linear structures from the same input stroke.}
+  \label{fig:edgeDir}
+\end{figure}
+
+On that example, when a straight features detection is run
+(\RefFigure{fig:edgeDir} a)),
+a thick blurred segment which extends up to four tiles is provided.
+When a straight edge detection is run, a very thin blurred segment is
+built to follow only one join edge.
+The multi-detection can also be applied to both thin object or edge detection.
+In the latter case, the detection algorithm is run twice using opposite
+directions, so that in the exemple of figure (\RefFigure{fig:edgeDir} b)),
+both edges (in different colours) are highlighted.
+These two thin blurred segments are much shorter, probably because the
+tiles are not perfectly aligned.
+This example illustrates our detector versatility.
 
-Algo de multi-detection.
+\subsection{Automatic blurred segment detection}
 
-\subsection{Automatic detection}
+Explication et b\'etail de l'algo
 
 \subsection{Implementation details}
 
 A directional scanner is encoded as an iterator that provides successively
 all the scan lines.
-
-Description de l'interface pour la d\'etection supervis\'ee d'un segment.
-
-Description de la d\'etection multiple.
-
-Description de la d\'etection automatique.

Article/notions.tex

-\section{Base notions}
+\section{Theoretical background}
 
 \subsection{Blurred segment}