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diff --git a/Expes/Analyses/CompDetectors/readme.txt b/Expes/Analyses/CompDetectors/readme.txt
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index 0000000000000000000000000000000000000000..c932e8c15dbfe772c0da88426612df9b375e86b5
--- /dev/null
+++ b/Expes/Analyses/CompDetectors/readme.txt
@@ -0,0 +1 @@
+pour construire compTable.tex
diff --git a/Methode/answerToReview.tex b/Methode/answerToReview.tex
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index 0000000000000000000000000000000000000000..3654ce1bee39ee9d3ab909548873dbc7798da840
--- /dev/null
+++ b/Methode/answerToReview.tex
@@ -0,0 +1,275 @@
+\documentclass{article}
+\usepackage[latin1]{inputenc}
+\usepackage{a4wide}
+\usepackage{graphicx}
+\usepackage{url}
+\usepackage{psfrag}
+\usepackage{pst-all}
+\usepackage{framed}
+\definecolor{dblue}{rgb}{0.2,0.2,0.6}
+
+
+\title{Answer to the reviews of the paper: \\ 
+Thick Line Segment Detection with Fast Directional Tracking}
+
+\author{P. Even*, P. Ngo, and B. Kerautret}
+
+\newcommand{\ok}{\newline {\color{blue} \underline{Corrected.}}}
+
+\newenvironment{answer}
+               { \begin{framed}
+                   \begingroup\color{blue}%
+                   \textbf{Answer:} }
+               {\endgroup\end{framed}}
+
+%\newenvironment{answer}{\\  \color{blue}\noindent\it {\underline{Answer:}}}%
+
+\newenvironment{todo}{\\  \color{red} \noindent\it {\underline{\textbf{TODO:} }}}%
+
+\begin{document}
+
+  \maketitle
+
+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}
+Effectively we were not aware of this recent reference.
+But contrarily to the other methods were no parameter had to be set for
+comparisons, here the setting of the ranking level parameter could largely
+influence achieved results.
+Moreover, as it is written in Matlab, effective comparison of time performance
+would require a complete re-programming in C-like language, with always
+possible rewritting bias.
+Therefore we add this reference in the state-of-the-art, but we can not
+take it into account in the experiments.
+\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}
+It 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}
+A pr\'eciser dans le texte : ... set to the observed thickness augmented by a
+tolerance factor of a half pixel able to take into account all the possible
+discrete lines which digitization fit to the selected points. 
+\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}
+An example synthetic image is already available in the mentioned github.
+We complete it with detailed views and respective performance.
+\begin{todo}
+Github \`a compl\'eter.
+\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 performance results are obtained using supervised mode.
+Actually, the supervised algorithm relies on the multiple unsupervised
+algorithm, featuring all the method improvements.
+Interactive aspects were more largely discussed in the former paper, on
+a visual validation basis in 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 are also
+valid for one or several manually extracted 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 aims at assessing the improved performance
+beween formal and actual versions of the detector, including the thickness
+value estimation. We test here a thickness ranging from 2 up to 5 pixels.
+Therefore the initial assigned thickness is set to a greater value : 7. \\
+For comparisons with other detectors, we restrict this thickness to a more
+suitable value to what can be provided by the other detectors, that are not
+designed to process too scattered lines. A value of pixels seems appropriate.
+\begin{todo}
+Pour la partie tests sur images de synth\`ese, ajouter
+... to detect all the lines in the defined thickness range in unsupervised
+mode. \\
+Dans la partie comparaison avec les autres, ajouter :
+... is set to 3 pixels, as the other detectors are designed to find thin lines.
+\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.
+Results of both versions are not easy to visually detect.
+So we add all these informations in the github, with the completed table.
+\begin{todo}
+De m\'emoire, il me semble qu'on obtient rien de significatif, voir pire,
+des valeurs meilleures que la nouvelle version car les segments sont du coup
+moins fractionn\'es. Je v\'erifie...
+\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 completed.
+\begin{todo}
+\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}
+
+\end{document}