Article : improved expes and conclusion

Article/abstract.tex

@@ -6,5 +6,8 @@ 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
 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, 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 lines in a single image.
+The new thick line detector is left available in an online demonstration.

Article/conclusion.tex

@@ -2,9 +2,9 @@
 
 \label{sec:conclusion}
 
-This paper introduced a new straight edge detector based on a local analysis of
+This paper introduced a new straight line detector based on a local analysis of
 the image gradient and on the use of blurred segments to embed an
-estimation of the detected edge thickness.
+estimation of the line thickness.
 It relies on directional scans of the input image around maximal values of the
 gradient magnitude, and on
 %that have previously been presented in \cite{KerautretEven09}.
@@ -16,28 +16,34 @@ gradient magnitude, and on
 %to the detected blurred segment direction;
 %The main limitations of the former approach were solved through
 the integration of two new concepts:
-adaptive directional scans that continuously adjust the scan strip
-to the detected edge direction, and
-control of assigned thickness based on the observation of the
-blurred segment growth.
-Experiments on synthetic images show the better performance
-and especially the more accurate estimation of the line thickness brought by
-these concepts.
-Such a result can not be compared to other approaches since they do not
-provide any thickness estimation.
-Moreover the performance of the unsupervised mode gives better coverage of
-the detected edges and produces quite comparable execution time.
+adaptive directional scans
+%that continuously adjust the scan strip to the detected edge direction,
+and control of assigned thickness.
+%based on the observation of the blurred segment growth.
+%Experiments on synthetic images show the better performance
+%and especially the more accurate estimation of the line thickness brought by
+%these concepts.
+%Such a result can not be compared to other approaches since they do not
+%provide any thickness estimation.
+%Moreover the performance of the unsupervised mode gives better coverage of
+%the detected edges and produces quite comparable execution time.
+Comparisons to other recent line detectors show competitive global
+performance in terms of execution time and mean length of output lines,
+while experiments on synthetic images indicate a better estimation of
+length and thickness measurements brought by the new concepts.
 
 A residual weakness of the approach is the sensitivity to the initial
 conditions.
 In supervised context, the user can select a favourable area where
 the awaited edge is dominant.
-This task is made quite easier, thanks to the stabilization produced by
-the duplication of the initial detection.
+%This task is made quite easier, thanks to the stabilization produced by
+%the duplication of the initial detection.
 But in unsupervised context, gradient perturbations in the early stage of
-the edge expansion, mostly due to the presence of close edges, can deeply
+the line expansion, mostly due to the presence of close edges, can
+% deeply
 affect the result.
-In future works, we intend to provide solutions to this drawback
+In future works, we intend to provide solutions
+% to this drawback
 by scoring the detection result on the basis of a characterization of the
 local context.
 %

Article/expeV2.tex

@@ -41,22 +41,29 @@ For all these experiments, the stroke sweeping step is set to 15 pixels.
 
 At first, the benefits of introduced concepts are evaluated through a
 comparison of the performance of both versions of the detector
-on a set of 1000 synthesized images containing 10 randomly
+(with and without the concepts) on a set of 1000 synthesized images
+containing 10 randomly
 placed input segments with random thickness between 2 and 5 pixels.
-As these values are controlled, these images can be considered as a
-ground truth.
+Such controlled images can be considered as ground truths.
+The initial assigned thickness $\varepsilon_0$ was 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.
 %On these synthetic images, the numerical error on the gradient extraction
 %biases the line width measures. This bias was first estimated using 1000
 %images containing only one input segment (no possible interaction)
 %and the found value (1.4 pixel) was taken into account in the test.
-\RefTab{tab:synth} shows
-slightly better thickness and angle measurements for the new detector.
-The new detector shows more precise, with a smaller amount of false
-detections and succeeds in finding most of the input segments.
-Other experiments, also available at the {\it GitHub} repository, show
-that the new detector is faster and finds more edges than the previous one.
+Results in \RefTab{tab:synth} show that the new concepts afford
+improved thickness and angle measurements, better precision
+with a smaller amount of false detections, and that they help to find
+most of the input segments.
+%\RefTab{tab:synth} shows
+%slightly better thickness and angle measurements for the new detector.
+%The new detector shows more precise, with a smaller amount of false
+%detections and succeeds in finding most of the input segments.
+Other experiments, also available at the {\it GitHub} repository, confirm
+these improvements.
+% than the previous one.
 
 
 %\begin{figure}[h]
@@ -103,8 +110,12 @@ ED-Lines \cite{AkinlarTopal12} and CannyLines \cite{LuAl15}.
 The tests are run on the York Urban database \cite{DenisAl08} composed
 of 102 images with their ground truth lines.
 As this database was set in the scope of Manhattan-world environments,
-only lines in the three main directions are identified.
+only lines in the three main directions are provided.
+Initial assigned thickness $\varepsilon_0$ is set to 3 pixels, and
+final length threshold to 10 points to suit the stroke sweeping step
+value.
 Results on one image are displayed in \RefFig{fig:york}.
+
 Compared measures $M$ are execution time $T$, covering ratio $C$,
 detected lines amount $N$, cumulated length of detected lines $L$ and
 mean length ratio $L/N$.
@@ -116,8 +127,7 @@ If we assume that a pixel of a ground truth line is identified
 if there is a detected line in its 8-neighborhood, then the measure $C$ is
 the mean ratio of the length of ground truth line pixels identified on the
 total amount of ground truth line pixels.
-For all these experiments, detected lines smaller than 10 pixels are
-discarded for all the detectors.
+Detected lines smaller than 10 pixels are discarded for all the detectors.
 Found measures are given in \RefTab{tab:comp}.
 
 \begin{figure}[h]