Article: Phuc's improvements

Article/Fig_method/algoMulti.tex

@@ -33,7 +33,7 @@
   \For{$i \leftarrow 0$ \KwTo \taille(\lm)}{
     \bseg $\leftarrow$ detect (\lm[i], \ortho, \eps, \mask)\;
     \updatemask (\mask, \bseg)\;
-    \bslist $\leftarrow$ \bseg\;
+    \bslist $\leftarrow$ \bslist + \bseg\;
   }
   \caption{MultiDetect: finds all segments crossing the selection stroke.}
 \end{algorithm}

Article/Fig_notions/bswidth.tex

@@ -26,5 +26,5 @@
   \put(160,61){\color{blue}{\vector(0,-1){10}}}
 %  \put(164,36){\color{blue}{$\mu_i$}}
   \put(164,30){\color{blue}{$\mu_i$}}
-  \put(180,60){\color{blue}{$\mathcal{B}_{i}$}}
+  \put(180,60){\color{blue}{$\mathcal{B}_{i} ?$}}
 \end{picture}

Article/abstract.tex

@@ -9,5 +9,5 @@ adaptive directional scans and control of assigned thickness.
 %Then, a new contribution to the automatic detection of all the segments in
 %a single image is also proposed and left available in an online demonstration.
 Then, these advances are exploited for a complete unsupervised detection of
-all the lines in a single image.
+all the line segments in an image.
 The new thick line detector is left available in an online demonstration.

Article/expeV2.tex

@@ -41,10 +41,11 @@ For all these experiments, the stroke sweeping step is set to 15 pixels.
 
 At first, the benefits of introduced concepts are evaluated through a
 comparison of the performance of both versions of the detector
-(with and without the concepts) on a set of 1000 synthesized images
+(with and without the concepts).
+The test is performed on a set of 1000 synthesized images
 containing 10 randomly
 placed input segments with random thickness between 2 and 5 pixels.
-Such controlled images can be considered as ground truths.
+%Such controlled images can be considered as ground truths.
 The initial assigned thickness $\varepsilon_0$ was set to 7 pixels
 to detect all the lines in unsupervised mode.
 The absolute value of the difference of each found segment to its
@@ -97,7 +98,7 @@ these improvements.
 \caption{Measured performance of both versions of the detector on a set of
 synthesized images.
 Old refers to the previous version \cite{KerautretEven09}, whereas new is
-the present detector (with adaptive directional scans and control of
+the proposed detector (with adaptive directional scans and control of
 assigned width).
 $S$ is the set of all the input segments,
 $D$ the set of all the detected blurred segments.}
@@ -123,7 +124,7 @@ On each image of the database we measure the execution time of 100 repetitions
 of a complete detection, gradient extraction included, for each line detector;
 $T$ is the mean value computed on the whole image set.
 Tests are run on Intel Core i5 processor.
-If we assume that a pixel of a ground truth line is identified
+Assuming that a pixel of a ground truth line is identified
 if there is a detected line in its 8-neighborhood, then the measure $C$ is
 the mean ratio of the length of ground truth line pixels identified on the
 total amount of ground truth line pixels.
@@ -173,5 +174,5 @@ on the York Urban Database \cite{DenisAl08}.}
 On these images, CannyLines provides longer lines and ED-Lines is much faster.
 Globally, the performance of the new detector is pretty similar and
 competitive to the other ones, and
-additionnaly, our detector provides an indication
-on the detected lines quality through the additional thickness parameter.
+furthermore, our detector provides an indication
+on the detected line quality through the estimated thickness.

Article/main.tex

@@ -28,7 +28,7 @@
 %           based on adaptive directional tracking of blurred segments}
 %%    \title{Fast Directional Tracking of Thick Line Segments}
     %% Proposition BK:
-        \title{Thick Line Segments Detection with Fast Directional Tracking}
+        \title{Thick Line Segment Detection with Fast Directional Tracking}
     \author{Philippe Even\inst{1} \and
             Phuc Ngo\inst{1} \and
             Bertrand Kerautret\inst{2}}

Article/methodV2.tex

@@ -11,7 +11,7 @@ stable appearance such as metallic tubular objects \cite{AubryAl17}.
 Contrarily to most detectors, no edge map is built here, but gradient
 magnitude and orientation are examined in privileged directions to track
 edge traces.
-Therefore we use a Sobel operator with a 5x5 pixels mask
+In particular, we use a Sobel operator with a 5x5 pixels mask
 to get high quality gradient information \cite{KekreGharge10}.
 
 \subsection{Previous work}
@@ -82,7 +82,7 @@ following sections \ref{subsec:ads} and \ref{subsec:caw}.
 Output segment $\mathcal{B}'$ is finally accepted based on application criteria.
 Final length and sparsity thresholds can be set accordingly.
 They are the only parameters of this local detector, together with the input
-assigned thickness.
+assigned thickness $\varepsilon_0$.
 %Too short, too sparse or too fragmented segments
 %can be rejected. Length, sparsity or fragmentation thresholds are
 %intuitive parameters left at the end user disposal.
@@ -150,7 +150,7 @@ the blurred segment all along the expansion stage.
 At each iteration $i$ of the expansion, the scan strip is aligned on the
 direction of the blurred segment $\mathcal{B}_{i-1}$ computed at previous
 iteration $i-1$.
-More formally, an adaptive directional scan $ADS$ is defined by:
+More formally, an {\it adaptive directional scan} $ADS$ is defined by:
 \begin{equation}
 ADS = \left\{
 S_i = \mathcal{D}_i \cap \mathcal{N}_i \cap \mathcal{I}
@@ -272,7 +272,7 @@ For each of them the main detection process is run with three modifications:
 \begin{enumerate}
 \item the initial detection takes $M_j$ and the orthogonal direction
 $\vec{AB}_\perp$ to the stroke as input to build a static scan of fixed
-thickness $2~\varepsilon_0$, and $M_j$ is used as start point of the
+thickness $2\cdot\varepsilon_0$, and $M_j$ is used as start point of the
 blurred segment;
 \item the occupancy mask is filled in with the points of the dilated blurred
 segments $\mathcal{B}_j'$ at the end of each successful detection

Article/notionsV2.tex

@@ -65,7 +65,7 @@ and $\mathcal{B}_i = \mathcal{B}_{i-1}$.}
 \end{figure}
 
 Associated to this primitive, the following definition of a directional scan
-is an important point of the proposed method.
+is an important point in the proposed method.
 
 \subsection{Directional scan}