Article: small reviewer precisions added

Article/expe.tex

@@ -24,7 +24,8 @@ At first, the performance of both versions of the detector (with and without
 the concepts) is tested on a set of 1000 synthesized images containing 10
 randomly placed input segments with random thickness between 2 and 5 pixels.
 The initial assigned thickness $\varepsilon_0$ is set to 7 pixels
-to detect all the lines in unsupervised mode.
+to detect all the lines
+\modifRev{in the defined thickness range} in unsupervised mode.
 The absolute value of the difference of each found segment to its
 matched input segment is measured.
 Results in \RefTab{tab:synth} show that the new concepts afford
@@ -53,7 +54,9 @@ of 102 images with their ground truth lines.
 As it was set in the scope of Manhattan-world environments,
 only lines in the three main directions are provided.
 For these experiments, initial assigned thickness $\varepsilon_0$ is set
-to 3 pixels, and final length threshold to 10 points to suit the stroke
+to 3 pixels,
+\modifRev{considering that the other detectors are designed to find thin lines,}
+and final length threshold to 10 points to suit the stroke
 sweeping step value.
 Output lines smaller than 10 pixels are discarded for all the detectors.
 Compared measures are execution time $T$, covering ratio $C$,
@@ -103,7 +106,9 @@ Results are given in \RefTab{tab:comp}.
 \centering
 \input{Tables/compTable}
 
-\caption{Measured performance of recent line detectors and of our detector
+\caption{Measured performance of recent line detectors
+\modifRev{(LSD \cite{GioiAl10}, ED-Lines \cite{AkinlarTopal12} and CannyLines
+\cite{LuAl15})} and of our detector
 on the York Urban Database \cite{DenisAl08}. }
 \label{tab:comp}
 \end{table}

Article/main.tex

@@ -22,6 +22,11 @@
 
 \input{macros}
 
+%answer to review
+\definecolor{dblue}{rgb}{0.2,0.2,0.8}
+\newcommand{\modifRev}[1]{{\color{dblue}{#1}}}
+%answer to review
+
 \begin{document}	
   \begin{frontmatter}
         \title{Thick Line Segment Detection with Fast Directional Tracking}

Article/method.tex

@@ -164,8 +164,8 @@ $C_{i-1}$, $\vec{D}_{i-1}$ and $\mu_{i-1}$ are respectively the intersection
 of the input selection and the central line of $\mathcal{B}_{i-1}$,
 the director vector of the optimal line of $\mathcal{B}_{i-1}$,
 and the thickness of $\mathcal{B}_{i-1}$.
-$\lambda$ is a delay which is set to 20 iterations to avoid direction instabilities
-when too few points are inserted.
+$\lambda$ is a delay which is set to 20 iterations to avoid direction
+instabilities when too few points are inserted.
 Compared to static directional scans where the scan strip remains fixed to
 the initial line $\mathcal{D}_0$, here the scan strip moves while
 scan lines remain fixed.
@@ -179,9 +179,12 @@ The assigned thickess $\varepsilon$ to the blurred segment recognition
 algorithm is initially set to a large value $\varepsilon_0$ in order to
 allow the detection of large blurred segments.
 Then, when no more augmentation of the blurred segment thickness is observed
-after $\tau$ iterations ($\mu_{i+\tau} = \mu_i$), it is set to a much
-stricter value able to circumscribe the possible interpretations of the
-segment, that take into account the digitization margins:
+after $\tau$ iterations ($\mu_{i+\tau} = \mu_i$), it is set to
+\modifRev{the observed thickness augmented by a half pixel tolerance factor,
+able to take into account all the possible discrete lines
+which digitization fits to the selected points.}
+%a much stricter value able to circumscribe the possible interpretations
+%of the segment, that take into account the digitization margins:
 \begin{equation}
 \varepsilon = \mu_{i+\tau} + \frac{\textstyle 1}{\textstyle 2}
 \end{equation}