From aafd9fd12182c60eb40a48b4946b344b7f90cb5f Mon Sep 17 00:00:00 2001
From: even <philippe.even@loria.fr>
Date: Wed, 3 Jul 2019 16:00:22 +0200
Subject: [PATCH] Answers : Phuc's modifications inserted

---
 Methode/answerToReview.tex     |  97 ++++++------
 Methode/answerToReview_PNG.tex | 268 ---------------------------------
 2 files changed, 53 insertions(+), 312 deletions(-)
 delete mode 100644 Methode/answerToReview_PNG.tex

diff --git a/Methode/answerToReview.tex b/Methode/answerToReview.tex
index 3654ce1..09b7789 100755
--- a/Methode/answerToReview.tex
+++ b/Methode/answerToReview.tex
@@ -9,7 +9,7 @@
 \definecolor{dblue}{rgb}{0.2,0.2,0.6}
 
 
-\title{Answer to the reviews of the paper: \\ 
+\title{Answer to the reviews of paper 65: \\ 
 Thick Line Segment Detection with Fast Directional Tracking}
 
 \author{P. Even*, P. Ngo, and B. Kerautret}
@@ -86,15 +86,14 @@ 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.
+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
@@ -108,9 +107,9 @@ 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
+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.
@@ -120,9 +119,7 @@ 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. 
+Done
 \end{todo}
 \end{answer}
 
@@ -130,10 +127,13 @@ discrete lines which digitization fit to the selected points.
 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.
+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.
 \begin{todo}
-Github \`a compl\'eter.
+Tableau \`a compl\'eter  avec les chiffres associ\'e \`a l'image dans le
+github.
 \end{todo}
 \end{answer}
 
@@ -142,17 +142,19 @@ 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
+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 affect} the supervised detection.
+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 are also
-valid for one or several manually extracted segments.
+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.
@@ -169,30 +171,31 @@ 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.
+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. \\
-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.
+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.
 \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.
+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.
-Results of both versions are not easy to visually detect.
-So we add all these informations in the github, with the completed table.
+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 if the the associated image is not displayed. \\
+We have added all these informations in the github, with the completed table.
+We just point out that achieved values have less meaning, because as explained
+in the paper, the lines detected by the former method are more likely to
+incorporate spurious points that artificially improve the width and length
+values.
 \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...
+Ajouter les images et la ligne dans le github.
 \end{todo}
 \end{answer}
 
@@ -202,8 +205,9 @@ 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.
+Thanks for this relevant suggestion. The caption is now completed.
 \begin{todo}
+Done.
 \end{todo}
 \end{answer}
 \end{itemize}
@@ -271,5 +275,10 @@ What are the most important factors in your rating? }
 \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}
diff --git a/Methode/answerToReview_PNG.tex b/Methode/answerToReview_PNG.tex
deleted file mode 100644
index 6a10a0f..0000000
--- a/Methode/answerToReview_PNG.tex
+++ /dev/null
@@ -1,268 +0,0 @@
-\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}
-Thanks for pointing this out. We have added this reference to the paper. However, we did 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, and could have 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}
-The 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}
-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}
-Due to page limitations, we could not add figure nor the respective performance result. However, an example of synthesized image is already available in the mentioned github, and we have completed it with detailed views and the 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 the performance results are obtained using the unsupervised mode. 
-However, the unsupervised algorithm is relied on the multiple supervised algorithm, therefore the results are also valid for the supervised detection.  
-Furthermore, the supervised mode concerns mainly in an interactive aspect, which were 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 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 aim at assessing the improved performance
-between 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. \\
-For the comparisons with other detectors, we restricted this thickness to a smaller value in order not to scatter the lines. The value 3 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}
-Due to page limitations, we could not extend Fig. 5.
-%Results of both versions are not easy to visually detect.
-However, we have added all these information 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 now 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 like to re-mention that our method additionally
-provides a measure of the line thickness without degrading the 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}
-\begin{answer}
-Thank you for your cautious reviewing of our paper. 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{itemize}
-
-\end{document}
-- 
GitLab