Article: intro revisited

Article/biblio.bib

@@ -102,3 +102,53 @@
   series = {LNCS},
   publisher = {Springer}
 }
+
+
+@article{GioiAl10,
+  title = {{LSD}: A Fast Line Segment Detector with a False Detection Control},
+  author = {Gioi, R. G. von and Jakubowicz, J.
+            and Morel, J.-M. and Randall, G.},
+  journal = {IEEE Trans on Pattern Analysis and Machine Intelligence},
+  volume = {32},
+  number = {4},
+  month = apr,
+  year = {2010},
+  pages = {722--732},
+  doi = {10.1109/TPAMI.2008.300}
+}
+
+
+@article{MatasAl00,
+  title = {Robust detection of lines using the progressive probabilistic
+           {H}ough transform},
+  author = {Matas, Jiri and Galambos, Charles and Kittler, Josef},
+  journal = {Computer Vision and Image Understanding},
+  volume = {78},
+  number = {1},
+  year = {2000},
+  pages = {119--137}
+}
+
+
+@inproceedings{LuAl15,
+  title = {CannyLines: A parameter-free line segment detector},
+  author = {Lu, Xiaohu and Yao, Jian and Li, Kai and Li, Li},
+  booktitle = {International Conference on Image Processing (ICIP)},
+  publisher = {IEEE},
+  year = {2015},
+  pages = {507--511}
+}
+
+
+@article{AkinlarTopal12,
+  title = {EDPF: a real-time parameter-free edge segment detector
+           with a false detection control},
+  author = {Akinlar, Cuneyt and Topal, Cihan},
+  journal = {International Journal of Pattern Recognition
+             and Artificial Intelligence},
+  volume = {26},
+  number = {01},
+  year = {2012},
+  pages = {1255002},
+  doi = {10.1142/S0218001412550026}
+}

Article/conclusion.tex

 \section{Conclusion and perspectives}
 
+\label{sec:conclusion}
+
 In this paper we introduced a new edge detector based on a local analysis of
 the image gradient and on the use of blurred segments to vehiculate an
 estimation of the edge thickness.
@@ -35,8 +37,15 @@ But this default remains quite sensible in unsupervised context.
 In future works, we intend to provide some protection against this drawback
 by scoring the detection result on the base of a characterization of the
 initial context.
-Then experimental validation of the consistency of the estimated
-width and orientation values on real situations are planned in
-different application fields.
+Then experimental validation of the consistency of the estimated width and
+orientation values on real situations are planned in different application
+fields.
+In particular, straight edges are rich visual features for 3D scene
+reconstruction from 2D images.
+The preimage of the detected blurred segments,
+i.e. the space of geometric entities which numerization matches this
+blurred segment, may be used to compute some confidence level in the 3D
+interpretations delivered, as a promising extension of former works
+on discrete epipolar geometry \cite{NatsumiAl08}.
 
 %\section*{Acknowledgements}

Article/expe.tex

 \section{Experimental validation}
 
+\label{sec:expe}
+
 The evaluation stage aims at quantifying the advantages of the new detector
 compared to the former one.
 For a fair comparison, the process flow of the former method (the initial

Article/intro.tex

 \section{Introduction}
 
+\label{sec:intro}
+
 \subsection{Motivations}
 
 Straight edge detection is a preliminary step of many image analysis
-processes. Therefore it is always an active reasearch topic centered
-on the quest of still faster, more accurate or more robust-to-noise
-methods.
-
-{\it TOWRITE : petit \'etat de l'art en r\'esumant IWCIA'09 et en ajoutant
-quelques id\'ees perso (Hough, local, ...).
-Parameter-space-based methods : robust to noise, well suited to
-supervided context \cite{EvenMalavaud00}.
-Most of works aim at reducing their time complexity. }
+processes. Therefore it is always an active research topic centered on
+the quest of still faster, more accurate or more robust-to-noise methods
+\cite{MatasAl00,GioiAl10,AkinlarTopal12,LuAl15}.
+However they seldom provide an exploitable measure of the output edge
+quality, based on intrinsic properties such as sharpness, connectivity
+or scattering.
+%Some information may sometimes be drawn from their specific context,
+%for example through an analysis of the peak in a Hough transform accumulator.
 
-These methods rarely provide a direct measure of the quality of the output
-edge, such as sharpness, connectivity or scattering.
-Some information may often be drawn from their specific context, for example
-through an analysis of the peak in a Hough transform accumulator, or
-TO COMPLETE.
-In particular, the accuracy of the edge orientation may be quite critical
-in some application contexts, such as computer vision.
+Digital geometry is a recent research domain where new mathematical definitions
+of quite classical geometric objects, such as lines or circles, are introduced
+to better fit to the discrete nature of most of todays data to process.
+In particular, the notion of blurred segment \cite{DebledAl05,Buzer07} was
+introduced to cope with the image noise or other sources of imperfections
+from the real world by the mean of a width parameter.
+Efficient algorithms were designed to recognize these digital objects in
+binary images.
 
-In digital geometry, the notion of blurred segment \cite{DebledAl05,Buzer07}
-was introduced to cope with the image noise or other sources of
-imperfections from the real world. The preimage of that geometrical object,
-ie the space of geometric entities which numerization matches this
-blurred segment, may convey useful information to evaluate possible moves in
-the 3D interpretations drawn, as a promising extension of former works
-on discrete epipolar geometry \cite{NatsumiAl08}.
-
-Our work aims at designing a flexible tool to detect such blurred segment
-in gray-level images for as well supervised as unsupervised contexts.
+Our work aims at designing a flexible tool to detect blurred segments
+with optimal width and orientation in gray-level images for as well
+supervised as unsupervised contexts.
 We seek for user-friendly solutions with ideally no parameter to set,
 or at least quite few values with intuitive meaning to an end user.
 
-\subsection{Method overview and previous work}
+\subsection{Previous work}
 
-The method we propose is based on a first rough detection in a local area
+In a former paper \cite{KerautretEven09}, we already introduced an efficient
+tool to detect blurred segments of fixed width in gray-level images. 
+It is based on a first rough detection in a local area
 of the image either defined by the user in supervised context or blindly
 explored in automatic mode. The goal is to disclose the presence of an edge.
 Therefore, a simple test as the gradient maximal value is performed.
 
 In case of success, refinement steps are run through an exploration of
 the image in the direction of the detected edge.
-In order to prevent local disturbances, for instance the intersection with
-a sharper edge, all the local gradient maxima are successively tested,
-and the gradient orientation consistency is checked.
+In order to prevent local disturbances such as the presence of a sharper
+edge nearby, all the local gradient maxima are successively tested
+untill a correct candidate with an acceptable gradient orientation is found.
+
+Only the gradient information is processed as it provides a good information
+on the image dynamics, and hence the presence of edges.
+Trials to also use the intensity signal were made though costly correlation
+techniques, but they were mostly successful for detecting objects with
+stable appearance such as metallic pipes \cite{AubryAl17}.
 
-We already designed and experimented an exploratory detector
-\cite{KerautretEven09}.
+Despite of good performances obtained compared to other methods from the
+literature, several drawbacks remain.
+First, the blurred segment width is not measured, but initially set by the
+user to meet the application requirements, so that no quality information
+can be derived from the computed segment.
+Moreover, the blurred segment hull is left free to shift sidewards, or worst,
+to rotate around a thin edge in the image, so that the produced orientation
+value can be largely biased.
 
-Despite of good performances obtained compared
-to other methods from the literature, several drawbacks remained.
-At first, a fixed width value was set by the user according to the
-application requirements, and detected segments were embedded in that
-fixed tolerence whatever their dispersion be. When this dispersion is low,
-the blurred segment is free to shiff sidewards, or worst, to rotate, thus
-degrading the provided position and rotation measures.
+Then, two refinement steps are systematically run to cope with most of the
+tested data, although this is useless when the first detection is successfull.
+Beyond, there is no guarantee that this could treat all kinds of data.
+The search direction is fixed by the detected direction at the former step,
+and there is necessarily a limit on this direction accuracy - at least
+linked to the restricted directions encoded in a limited grid - so that
+other steps would have been necessary to deal with high resolution images.
 
-Then two refinement steps were arbitrarily run to cope with most of
-the tested data, uselessly when the first one was successfull.
-Beyond, there was no guarantee that this could treat all kinds
-of data. The search direction is fixed by the detected direction at the
-former step, and there is necessarily a limit on this direction
-accuracy - at least linked to the restricted directions encoded
-in a limited grid - so that other steps would have been necessary
-to deal with high resolution images.
+\subsection{Main contritions}
 
-Our study relies only on the use of the image gradient, as it provides a
-good information on the signal dynamics, and hence the presence of edges.
-Trials were made to use also the intensity signal though expensive
-correlation techniques, but it was mostly successful for tracking objects
-with stable appearance such as metallic pipes \cite{AubryAl17}.
+The work presented in this paper aims at solving both former mentioned
+drawbacks through two main contributions:
+the concept of adaptive directional scanner designed to get some compliance
+to the unpredictable orientation problem;
+the control of the assigned width to the blurred segment recognition algorithm,
+intended to derive more reliable information on the edge orientation and
+quality.
+As a side effect, this two major evolutions led to a noticeable improvement
+of the execution time.
 
-Organisation of the paper : TO WRITE.
+In the next section, the main theoretical notions this work relies on are
+introduced, with a specific focus on the new concept of adaptive directional
+scanner.
+Then the new detector workflow and its integration into both supervised and
+unsupervised contexts are presented and discussed in \RefSec{sec:method}.
+Experiments led to assess the expected increase of performance are decribed
+in \RefSec{sec:expe}.
+Finally achieved results are summarized in \RefSec{sec:conclusion},
+followed by some open perspectives for future works.

Article/macros.tex

 \newcommand{\RefFig}[1]{Fig.\,\ref{#1}}
-\newcommand{\RefSec}[1]{Section\,\ref{#1}}
+\newcommand{\RefSec}[1]{section\,\ref{#1}}
 \newcommand{\RefTab}[1]{Tab.\,\ref{#1}}

Article/main.tex

 \documentclass[runningheads]{llncs}
 
-%\usepackage[utf8]{inputenc}
-%\usepackage[T1]{fontenc}
+\usepackage[utf8]{inputenc}
+\usepackage[T1]{fontenc}
 
 \usepackage{graphicx}
 %\graphicspath{{./Fignotions/}{./Figmethod}}
@@ -25,10 +25,11 @@
 
     \authorrunning{P. Even et al.}
 
-    \institute{Universit\'e de Lorraine, LORIA, UMR 7503, Nancy, France
-               \email{philippe.even,hoai-diem-phuc.ngo\{at\}loria.fr}
-               \and Universit\'e de Lyon 2, LIRIS, Lyon, France
-               \email{bertrand.kerautret\{at\}univ-lyon2.fr}}
+    \institute{Universit\'e de Lorraine, LORIA (UMR 7503), Nancy, France
+               \email{philippe.even@loria.fr},
+               \email{hoai-diem-phuc.ngo@loria.fr}
+               \and Universit\'e Lyon 2, LIRIS (UMR 5205), Lyon, France
+               \email{bertrand.kerautret@univ-lyon2.fr}}
 
     \maketitle
 

Article/method.tex

 \section{The detection method}
 
+\label{sec:method}
+
 \subsection{Workflow of the detection process}
 
-The work-flow of the blurred segment detection process is summerized
+The workflow of the blurred segment detection process is summerized
 in the following figure.
 
 \begin{figure}[h]
@@ -33,7 +35,7 @@ in the following figure.
     \put(330,18){\scriptsize $\mathcal{B}_3$}
     \put(322,15){\vector(1,0){22}}
   \end{picture}
-  \caption{The detection process main work-flow.}
+  \caption{The detection process main workflow.}
   \label{fig:workflow}
 \end{figure}
 
@@ -123,7 +125,7 @@ the awaited one.
   \label{fig:voisins}
 \end{figure}
 
-This detection procedure can be used to dectect as well straight edges
+This detection procedure can be used to detect as well straight edges
 as thin straight objects. In the first case, the gradient vectors of all
 edge points are assumed to be oriented in the same direction. But if the
 sign of the gradient direction is not considered, points with gradient in

Article/notions.tex

 \section{Theoretical background}
 
+\label{sec:notions}
+
 \subsection{Blurred segment}
 
 Our work relies on the notion of digital straight line as classically
@@ -23,9 +25,9 @@ of points in $\mathbb{Z}^2$ that all belong to a digital line of
 arithmetical width $\varepsilon$.
 \end{definition}
 
-Linear time algorithms have been developed to recognize a blurred segment
-of assigned width $\varepsilon$ \cite{DebledAl05,Buzer07}.
-They are based on an incremental growth of the convex hull of the blurred
+In this work, we use a linear-time algorithm that was developed to recognize
+a blurred segment of assigned width $\varepsilon$ \cite{DebledAl05}.
+It is based on an incremental growth of the convex hull of the blurred
 segment when adding each point successively.
 The minimal width $\mu$ of the blurred segment $\mathcal{B}$ is the
 arithmetical width of the narrowest digital straight line that contains