Article : last corrections

Article/abstract.tex

@@ -3,11 +3,9 @@ detector in gray-level images,
 where line segments are enriched with a thickness parameter
 intended to provide a quality criterion on the extracted feature.
 This study is based on a previous work on interactive line detection
-in gray level images. At first, a better estimation of the segment thickness
+in gray-level images. At first, a better estimation of the segment thickness
 and orientation is achieved through two main improvements:
 adaptive directional scans and control of assigned thickness.
-%Then, a new contribution to the automatic detection of all the segments in
-%a single image is also proposed and left available in an online demonstration.
 Then, these advances are exploited for a complete unsupervised detection of
 all the line segments in an image.
 The new thick line detector is left available in an online demonstration.

Article/biblio.bib

@@ -290,3 +290,30 @@ in urban imagery},
  optdoi = {10.1145/2817675.2817689},
  publisher = {ACM}
 }
+
+
+@article{XuAl17,
+  title = {Pose estimation from line correspondences: a complete analysis
+           and a series of solutions},
+  author = {Xu, Chi and Zhang, Lilian and Cheng, Li and Koch, Reinhard},
+  journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
+  volume = {39},
+  number = {6},
+  optmonth = {June},
+  year = {2017},
+  pages = {1209--1222},
+  optdoi = {10.1109/TPAMI.2016.2582162}
+}
+
+
+@inproceedings{LeeAl09,
+ author = {Lee, David C. and Hebert, Martial and Kanade, Takeo},
+ title = {Geometric reasoning for single image structure recovery},
+ booktitle = {IEEE International Conference
+              on Computer Vision and Pattern Recognition},
+ year = {2009},
+ optlocation = {Miami, Florida},
+ pages = {2136--2143},
+ optdoi = {10.1109/CVPR.2009.5206872},
+ optpublisher = {ACM}
+}

Article/expeV2.tex

@@ -33,7 +33,7 @@ addressed in this work, so that it can not be actually compared to the
 thickness value output by the new detector.
 Moreover, we did not find any data base with ground truth including
 line thickness.
-Therefore we proceed in two steps :
+Therefore, we proceed in two steps:
 (i) evaluation on synthetic images of the new concepts enhancements
 on line orientation and thickness estimation;
 (ii) evaluation of more global performance of the proposed approach
@@ -45,7 +45,7 @@ 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.
 %Such controlled images can be considered as ground truths.
-The initial assigned thickness $\varepsilon_0$ was set to 7 pixels
+The initial assigned thickness $\varepsilon_0$ is set to 7 pixels
 to detect all the lines in unsupervised mode.
 The absolute value of the difference of each found segment to its
 matched input segment is measured.
@@ -165,8 +165,8 @@ on the York Urban Database \cite{DenisAl08}. }
 \label{tab:comp}
 \end{table}
 
-Results displayed on the example of \RefFig{fig:york} indicate that 
-the new detector produces many small segments, that could be considered as
+The example of \RefFig{fig:york} indicates that 
+the new detector produces many small segments which could be considered as
 visually non-meaningful. The other detectors eliminates them by a
 validation test based on Helmholtz principle \cite{DesolneuxAl08}.
 Such test is not yet integrated into the new detector.

Article/introV2.tex

@@ -8,9 +8,10 @@ processes.
 %points associated to main directions of the 3D world, thus allowing to compute camera
 %orientation. They are also used to detect structured features for
 %3D reconstruction.
-In particular with man-made environments, they are a suitable alternative
-to points for camera orientation \cite{DenisAl08}, 3D reconstruction
-\cite{HoferAl17,ParkAl15,ZaheerAl18}
+In particular in man-made environments, they are a suitable alternative
+to points for camera orientation \cite{DenisAl08,XuAl17}, 3D reconstruction
+%\cite{HoferAl17,ParkAl15,ZaheerAl18}
+\cite{ParkAl15,ZaheerAl18}
 or also simultaneous localization and mapping
 \cite{HiroseSaito12,RuifangAl17}.
 
@@ -55,7 +56,7 @@ User-friendly solutions are sought, with ideally no parameter to set,
 or at least quite few values with intuitive meaning.
 %A first attempt was already made in a previous work \cite{KerautretEven09}
 An interactive tool was already designed for live line extractions in
-gray level images \cite{KerautretEven09}.
+gray-level images \cite{KerautretEven09}.
 But the segment thickness was initially fixed by the user and not estimated,
 leading to erroneous orientations of the detected lines.
 Here, the limitations of this first detector are solved

Article/methodV2.tex

@@ -5,7 +5,7 @@
 In this line detection method, 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 through costly correlation
+Trials to use the intensity signal were also made through costly correlation
 techniques, but they were mostly successful for detecting shapes with a
 stable appearance such as metallic tubular objects \cite{AubryAl17}.
 Contrarily to most detectors, no edge map is built here, but gradient
@@ -27,7 +27,7 @@ In case of success, refinement steps were then run through an exploration of
 the image in the direction of the detected edge.
 In order to prevent local disturbances such as the presence of a sharper
 edge nearby, all the local gradient maxima were successively tested
-untill a correct candidate with an acceptable gradient orientation was found.
+until a correct candidate with an acceptable gradient orientation was found.
 
 Despite of a good behavior reported, several drawbacks remained.
 First, the blurred segment thickness was not measured but initially set by the
@@ -162,16 +162,16 @@ S_i = \mathcal{D}_i \cap \mathcal{N}_i \cap \mathcal{I}
 i > \lambda
 \end{array} \right. \right\}
 \end{equation}
-where $C_{i}$, $\vec{D}_{i}$ and $w_{i}$ are respectively a position,
+where $C_{i}$, $\vec{D}_{i}$ and $\mu_{i}$ are respectively a position,
 a director vector and a thickness observed at iteration $i$,
 used to update the scan strip and lines in accordance to \RefEq{eq:dsdef2}.
 %In the scope of the present detector,
-The last clause expresses the update of the scan bounds at iteration $i$ :
+The last clause expresses the update of the scan bounds at iteration $i$:
 $C_{i-1}$, $\vec{D}_{i-1}$ 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 set to 20 iterations to avoid direction instabilities
+$\lambda$ 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