diff --git a/Article/Fig_method/algoMulti.tex b/Article/Fig_method/algoMulti.tex
index 2804c8932824beb0fc678d736c0a68990f0fe824..341e8f33242f42828e19ed97ebb8482341e411fb 100644
--- a/Article/Fig_method/algoMulti.tex
+++ b/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}
diff --git a/Article/Fig_notions/bswidth.tex b/Article/Fig_notions/bswidth.tex
index e37cc960361656016cd9d4da010f0660e66a00c7..05c3d6948bfb817f88ff8daec54ef9ebd8c34127 100644
--- a/Article/Fig_notions/bswidth.tex
+++ b/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}
diff --git a/Article/abstract.tex b/Article/abstract.tex
index acaba1eb466bbec2e437a8cb3c7d757da5f971a0..caea7ed5546f34dc4222d9e4851a07d13d2e0ba3 100755
--- a/Article/abstract.tex
+++ b/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.
diff --git a/Article/expeV2.tex b/Article/expeV2.tex
index 99eb481e52a32d89dcd052e231465d7cf3729d54..c4f8cd6cec8fcace4c6ba2b42c5664ca61130d16 100755
--- a/Article/expeV2.tex
+++ b/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.
diff --git a/Article/main.tex b/Article/main.tex
index 6db042eb752df9e0a622de48d37de09867ac1973..b83d48488b5c63fa8c7d5b6a3261598a87295009 100755
--- a/Article/main.tex
+++ b/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}}
diff --git a/Article/methodV2.tex b/Article/methodV2.tex
index 5c7ddcfec8767a83120ecb019ca6f89bfeb6cd62..e82c7a41577701690995b97b646f8bc0d5113a59 100755
--- a/Article/methodV2.tex
+++ b/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
diff --git a/Article/notionsV2.tex b/Article/notionsV2.tex
index fd11d743eae988917f8c7bd0e3ea7dadc4561d5a..bf161823d1948828061bba22d528984e4213c490 100755
--- a/Article/notionsV2.tex
+++ b/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}