Article: enhancements

Article/conclusion.tex

@@ -6,7 +6,8 @@ This paper 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.
 It relies on directional scans of the image around maximal values of the
-gradient magnitude, that have previously been presented in a former paper.
+gradient magnitude, that have previously been presented in
+\cite{KerautretEven09}.
 Despite of good performances obtained compared to existing detection methods
 found in the literature, the former approach suffers of two major drawbacks.
 It does not estimate the edge thickness so that many outliers are inserted
@@ -22,8 +23,8 @@ the control of the assigned width based on the observation of the
 blurred segment thickenning in the early stage of its expansion.
 
 Expected gains in execution time linked to the suppression of a useless
-repetition of the fine tracking stage were confirmed by the experimental
-campaign both in supervised and unsupervised contexts.
+repetition of the fine tracking stage were confirmed by the experiments
+both in supervised and unsupervised contexts.
 The residual weakness is the high sensitivity to the initial conditions
 despite of the valuable enhancement brought by the duplication of the
 initial detection.
@@ -34,7 +35,7 @@ In supervised context, the user can easily select a favourable area where
 the awaited edge is dominant.
 But this default remains quite sensible in unsupervised context.
 
-In future works, we intend to provide some protection against this drawback
+In future works, we intend to provide some solutions for 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

Article/expe.tex

@@ -5,15 +5,13 @@
 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
-detection followed by two refinement steps) was coded as an option of the
-new detector, because since that time, the code of basic routineshas
-largely been improved.
-For instance, the new directional scanners are encoded as an iterator so that
-only required scan lines are provided when required, whereas with the former
-code, all the scan lines were computed and returned, whenever they were used
-or not.
-Outputs of the new coding of the former detector were compared to those of
-the old release, to ensure an equivalent behaviour.
+detection followed by two refinement steps) is integrated as an option
+into the code of the new detector, so that both methods rely on the same
+optimized basic routines.
+
+The first test compares the computation times of both detectors on a
+selection of input strokes (\RefFig{fig:buro}). Results are displayed
+in \RefTab{tab:cmpOldNew}.
 
 \begin{figure}[h]
 \center
@@ -46,10 +44,6 @@ the old release, to ensure an equivalent behaviour.
   \label{fig:buro}
 \end{figure}
 
-The first test compares the computation times of both detectors on a
-selection of input strokes (\RefFig{fig:buro}). Results are displayed
-in table \RefTab{tab:cmpOldNew}.
-
 \begin{table}
 \centering
 \begin{tabular}{|l||l|l|l|l|l|l|l|l|l|l|}
@@ -87,7 +81,7 @@ former detector on the left, and the new detector on the right.}
   \label{fig:evalAuto}
 \end{figure}
 
-The former detector do not estimate the edge width, but just circumscribes
+The former detector does not estimate the edge width, but just circumscribes
 the edge with a blurred segment of assigned width.
 If the edge is very thin, the blurred segment is free to rotate around the
 extracted edge and the provided orientation is biased.

Article/method.tex

@@ -67,7 +67,7 @@ edge he wants to extract from the image.
 The detection method previously described is continuously run during mouse
 dragging and the output blurred segment is displayed on-the-fly.
 
-The method is very sensitive to the local conditions of the initial detection
+The method is quite sensitive to the local conditions of the initial detection
 so that the output blurred segment may be quite unstable.
 In order to temper this undesirable behaviour for particular applications,
 the initial detection can be optionally run twice, the second fast scan being
@@ -85,11 +85,11 @@ For each of them the main detection process is run with three modifications:
 i) the initial detection takes $M_j$ and the orthogonal direction $AB_\perp$
 to the stroke as input to build a static scan of fixed width
 $\varepsilon_{ini}$, and $M_j$ is used as start point of the blurred segment;
-ii) a occupancy mask, initially empty, is filled in with the points of the
-detected blurred segments $\mathcal{B}_{j3}$ at the end of each successful
+ii) an occupancy mask, initially empty, is filled in with the points of the
+detected blurred segments $\mathcal{B}_j$ at the end of each successful
 detection;
 iii) points marked as occupied are rejected when selecting candidates for the
-blurred selegment extension in the fine tracking step.
+blurred segment extension in the fine tracking step.
 
 \input{Fig_method/algoMulti}
 
@@ -112,11 +112,11 @@ the awaited one.
     \parbox{0.22\textwidth}{\centering{\scriptsize{c)}}} &
     \parbox{0.22\textwidth}{\centering{\scriptsize{d)}}}
   \end{tabular}
-  \caption{Example of edge, detected only in multi-detection mode:
-    a) the stroke on the intensity image,
+  \caption{Example of edge disclosed by the multi-detection mode:
+    a) the input selection on the intensity image,
     b) the gradient map,
-    c) the result of the classical single mode detection,
-    d) the result of the multi-detection. }
+    c) the only sharper edge detected in classical single mode,
+    d) a neighbouring edge disclosed by the multi-detection mode. }
   \label{fig:voisins}
 \end{figure}
 
@@ -143,14 +143,14 @@ the tile joins of \RefFig{fig:edgeDir}.
   \label{fig:edgeDir}
 \end{figure}
 
-On that example, when a straight features detection is run
-(\RefFig{fig:edgeDir} a)),
+On that example, when a straight feature detection is run
+(\RefFig{fig:edgeDir} a),
 a thick blurred segment which extends up to four tiles is provided.
 When a straight edge detection is run, a very thin blurred segment is
 built to follow only one join edge.
 The multi-detection can also be applied to both thin object or edge detection.
 In the latter case, the detection algorithm is run twice using opposite
-directions, so that in the exemple of figure (\RefFig{fig:edgeDir} b)),
+directions, so that in the exemple of figure (\RefFig{fig:edgeDir} b),
 both edges (in different colours) are highlighted.
 These two thin blurred segments are much shorter, probably because the
 tiles are not perfectly aligned.