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\title{Answer to the reviews of paper 65: \\ 
Thick Line Segment Detection with Fast Directional Tracking}

\author{P. Even*, P. Ngo, and B. Kerautret}

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We would like to thank the editors and reviewers for their work and
for their constructive comments, questions and suggestions.
Because the paper already reaches the 10 pages limit, and in order to
avoid removal of possibly valuable contents for paper understanding,
additional data are put on the github, that is referenced in the paper.
A detailed list of the changes is given below with also some specific
answers to raised questions.
{\color{blue} \bf Our paper is attached to our answer and the proposed
modified text is highlighted in blue color}.



\section{Reviewer \#4}

\begin{itemize}
\item {\bf 1. Summary. In 3-5 sentences,
describe the key ideas and experiments and their significance.}
\begin{itemize}
\item A straight line detector in gray-scale images is proposed in this paper.
Proposed method is an extension of a previous work of the same author.
Novelties brang in this version are 1) adaptive directional scan which
provides extending the segment through apropriate direction, and 2)
control of assigned thickness used in blurred segment recognition,
where an initially assigned large thickness value is tuned regarding
to the amount of augmentation at blurred thickness observed in subsequent
iterations.
\end{itemize}

\item {\bf 2. Strengths. Consider the significance of key ideas,
experimental validation, writing quality. Explain clearly why these aspects
of the paper are valuable.}
\begin{itemize}
\item Experimenting on the synthesized images, it is shown that the proposed
method brings performance improvement compared to the older version of the
method.
\item At the experiment done on the York Urban database, three literature
works are outperformed in terms of two evalutation metrics, i.e. C and L/N.
\item It is appreciated that the code of the work is published which
encourages reproducibility.
\item Writing quality of the paper is good.
\end{itemize}

\item {\bf 3. Weaknesses. Consider significance of key ideas, experiments,
writing quality. Clearly explain why these are weak aspects of the paper,
e.g. why a specific prior work has already demonstrated the key contributions,
or why the experiments are insufficient to validate the claims.}
\begin{itemize}
\item Although performance improvement is achived comparejd to the previous
version of the method, it is not compared with the latest state-of-the-art
methods (e.g. [Almazan, E.J., Tal, R., Qian, Y. and Elder, J.H., 2017.
Mcmlsd: A dynamic programming approach to line segment detection.
In Proceedings of the IEEE Conference on Computer Vision and Pattern
Recognition (pp. 2031-2039).]), but mainly with three literature works,
latest of which was published in 2015. \\

\begin{answer}
Thanks for pointing this out. We have added this reference to the paper.
However, we do not consider it in the experiments,
because contrarily to the other methods in which no parameter has to be set
for comparisons, this method has several ranking level parameters which could
largely influence achieved results.
Moreover, the code is written in Matlab. To ensure a fair comparison of time
performance, it would require a complete re-programming in C-like language,
that could produce possible rewritting bias.
\begin{todo}
Ajouter la publi dans l'etat de l'art et un laius dans la partie expe
pour dire qu'il n'est pas immediat de se comparer avec, et qu'on ne retient
que les m\'ethodes les plus proches en terme d'architecture.
DETAIL : voir si le protocole de comparaison propos\'e peut \^etre pris
en compte dans la r\'eponse.
\end{todo}
\end{answer}

Some notes are as belows:
\begin{itemize}
\item What is the reason to sum the blurred segment thickness by 1/2 in Eq. 5?
\begin{answer}
This value comes from a discrete geometry consideration linked to the space
of all discrete lines, that could provide the observed digitization (called
the pre-image). For instance, if we observe points on the same line, the
measured width is 1 pixel. If we set the assigned thickness to this value,
then we exclude possible one-line steps (well-known aliasing effect) for
nearly horizontal lines.
This value of 1/2 is assumed to bound all the possible expansions of the
observed line. \\
The text was changed to precise the role of this half pixel margin.
But of course, we have no space left to discuss all these discrete geometry
considerations in the paper.
\begin{todo}
Done
\end{todo}
\end{answer}

\item Could the authors present a couple of example for sythesized images
that were used in the experiments and the performance of both versions of
the method obtained on them ?
\begin{answer}
Due to page limitations, we could not add any figure nor respective
performance result in the paper. However, an example of synthesized image is
already available in the mentioned github, and we have completed the
table with associated results. Accordingly to the measured standard deviations
obtained on the whole set of 1000 randomly generated images, observed results
on individual images can largely change.
\begin{todo}
Done. Second example ?
\end{todo}
\end{answer}

\item What is understood from the paper is the performance results presented
in Table 1 and 2 are obtained using unsupervised mode. Is it correct?
Then, why the performance obtained by supervised mode was not also presented,
despite Section 3.5 is allocated for explanation of it?
\begin{answer}
We confirm that the performance results are obtained using the unsupervised
mode.
However, as the unsupervised algorithm is based on the supervised multiple
line detection algorithm, these results {\bf also reflect} the supervised
detection performance.
Furthermore, the supervised mode deals mainly with an interactive aspect,
which was largely discussed in the former paper.
In this context, there is a lack of available ground truth directly
related to supervised line extraction.
We guess that such ground truth would closely depend on application
requirements.
Thus, we rather concentrate here on the automatic detection novelties,
assuming that achieved performance on all segments also impact the manual
detection of one or several line segments.
\begin{todo}
V\'eriier que dans la partie supervis\'ee, on ne pr\'esente rien de
sp\'ecifique, sinon, peut-\^etre qq chose \`enlever pour gagner de la place.
\end{todo}
\end{answer}

\item It is not clearly mentioned and justified why the initial thickness
values of 7 and 3 are used at the experiments of Table 1 and 2, respectively.
Since one of the main motivation of superity of this work compared to the
previous version is claimed as not using a fixed thickness input value,
the justification of selection of these initial parameter values should be
presented and performance related to selection of this parameter value
should also be presented and discussed.
\begin{answer}
Experiments on synthetic images aim at assessing the improved performance
beween formal and actual versions of the detector, including the thickness
value estimation. The synthesized images are generated with a thickness
varying from 2 up to 5 pixels.
Therefore the initial assigned thickness is set to a greater value : 7. \\
The other detectors aim at providing thin lines and may reject too
scattered image lines. To adapt to this behavior, we retrict the detection
to thin lines using the initial assigned thickness to 3 pixels. \\
The text of the paper has been precised accordingly.
\begin{todo}
Done.
\end{todo}
\end{answer}

\item I would like to see the performance of the previous version of the
method [11] at the York Urban dataset, in Table 2 and Fig. 5.
\begin{answer}
Unfortunately, we have no more left space to extend Fig. 5, where images
are already quite small.
It would maybe be possible to add one line in the table, but the interest is
weak in lack of the associated image. \\
We have added all the required informations in the github
(https://github.com/evenp/FBSD), with the completed
table (T, N and L values were already available, along with mean thickness W).
We notice that achieved values have less meaning here, because as explained
in the paper, the lines detected by the former method are more likely to
incorporate spurious points, that artificially grow the width and length
values.
\begin{todo}
Done.
\end{todo}
\end{answer}

\item I would suggest to the authors to give the reference numbers for the
methods ijn Table 2. If these numbers are obtained by the authors
but not got from the papers of LSD, ED-Lines and CannyLines, then it may be
better to refer to the methods with citing those papers at the caption of
Table 2.
\begin{answer}
Thanks for this relevant suggestion. The caption is now completed.
\begin{todo}
Done.
\end{todo}
\end{answer}
\end{itemize}
\end{itemize}

\item {\bf 4. Paper rating}
\begin{itemize}
\item Borderline
\end{itemize}

\item {\bf 5. Justification of rating.
What are the most important factors in your rating? }
\begin{itemize}
\item A contribution of this work is it brings perfokkrmance improvement
compared to the previous published version of the method.
However, it is still not clear how the proposed method close to the state
of the art performaonces reported by recent papers.
\begin{answer}
We would just like to insist on the fact that our method additionnally
provides a measure of the line thickness without degrading other performance
with respect to some other recent detectors.
\end{answer}
\end{itemize}
\end{itemize}


\section{Reviewer \#5}

\begin{itemize}
\item {\bf 1. Summary. In 3-5 sentences,
describe the key ideas and experiments and their significance.}
\begin{itemize}
\item This paper considers the problem of detecting straight lines
in grey-scale images, that is an essential task in several Computer Vision
applications. The proposed approach is based on adaptive directional scans
and control of assigned thickness, and it is evaluated on both synthetic and
real scenarios.
\end{itemize}

\item {\bf 2. Strengths. Consider the significance of key ideas,
experimental validation, writing quality. Explain clearly why these aspects
of the paper are valuable.}
\begin{itemize}
\item Experimental results report good performances.
The authors provide both the code and an online demo.
\end{itemize}

\item {\bf 3. Weaknesses. Consider significance of key ideas, experiments,
writing quality. Clearly explain why these are weak aspects of the paper,
e.g. why a specific prior work has already demonstrated the key contributions,
or why the experiments are insufficient to validate the claims.}
\begin{itemize}
\item As acknowledge by the authors, the method is sensitive to the initial
conditions.
\end{itemize}

\item {\bf 4. Paper rating}
\begin{itemize}
\item Accept
\end{itemize}

\item {\bf 5. Justification of rating.
What are the most important factors in your rating? }
\begin{itemize}
\item Experiments clearly demonstrate that this paper is a valid contribution.
\end{itemize}
\end{itemize}
\begin{answer}
Thank you for reviewing our paper. As mentioned in the conclusion,
we understand the weekness of the proposed method related to the initial
conditions, we try to overcome these limitations in future works.
\end{answer}

\end{document}
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