Article: intro revisited

Article/intro.tex

@@ -37,7 +37,7 @@ blurred segments of fixed width in gray-level images was already introduced.
 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.
+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.
@@ -48,25 +48,23 @@ 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 through costly correlation
-techniques, but they were mostly successful for detecting objects with
-stable appearance such as metallic pipes \cite{AubryAl17}.
+techniques, but they were mostly successful for detecting shapes with a
+stable appearance such as metallic tubular objects \cite{AubryAl17}.
 
-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, and the produced orientation
-value can be largely biased.
+Despite of good performances achieved, several drawbacks remain.
+First, the blurred segment width is not measured but initially set by the
+user according to the application requirements. The produced information
+on the edge quality is rather poor, and especially when the edge is thin,
+the risk to incorporate outlier points is quite high, thus producing a
+biased estimation of the edge orientation.
 
-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 support vector of the blurred segment
-detected at the former step, and because the set of vectors in a bounded
-discrete space is finite, there is necessarily a limit on this direction
-accuracy.
+Then, two refinement steps are systematically run.
+On one hand, this is useless when the first detection is successfull.
+On the other hand, there is no guarantee that this approach is able to
+process larger images.
+The search direction relies on the support vector of the blurred segment
+detected at the former step, and the numerization rounding fixes a limit
+on this estimated orientation accuracy.
 It results that more steps would inevitably be necessary to process higher
 resolution images.
 
@@ -83,10 +81,10 @@ As a side effect, these two major evolutions also led to a noticeable
 improvement of the time performance of the detector.
 
 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}.
+introduced.
+Then the new detector workflow, the adaptive directional scanner, the control
+of the assigned with and their 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},

Article/method.tex

@@ -267,7 +267,7 @@ This distinction is illustrated on \RefFig{fig:edgeDir}.
 
 Another option, called multi-detection allows the detection of all the
 segments crossed by the input stroke $AB$.
-The multi-detection algorithm is displayed below.
+The multi-detection algorithm (Algorithm 1) is displayed below.
 
 \input{Fig_method/algoMulti}
 
@@ -282,6 +282,7 @@ 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 segment extension in the fine tracking step.
+Multiple detections of the same edge are thus avoided.
 
 In edge selection mode (\RefFig{fig:edgeDir} b), the multi-detection
 algorithm is executed twice.
@@ -341,7 +342,8 @@ segment.
 \subsection{Automatic blurred segment detection}
 
 An unsupervised mode is also proposed to automatically detect all the
-straight edges in the image. A stroke that crosses the whole image, is
+straight edges in the image. The principle of this automatic detection
+is described in Algorithm 2. A stroke that crosses the whole image, is
 swept in both direction, vertical then horizontal, from the center to
 the borders. At each position, the multi-detection algorithm is run
 to collect all the segments found under the stroke.

Article/notions.tex

@@ -67,7 +67,7 @@ DS = \left\{ S_i = \mathcal{D} \cap \mathcal{N}_i \cap \mathcal{I}
 \end{array} \right. \right\}
 %S_i = \mathcal{D} \cap \mathcal{N}_i, \mathcal{N}_i \perp \mathcal{D}
 \end{equation}
-In this expression, the clause
+In this definition, the clause
 $\delta(\mathcal{N}_i) = - \delta^{-1}(\mathcal{D})$
 expresses the othogonality constraint between the scan lines $\mathcal{N}_i$
 and the scan strip $\mathcal{D}$.
@@ -79,7 +79,7 @@ The scans $S_i$ are developed on each side of a start scan $S_0$,
 and ordered by their distance to the start line $\mathcal{N}_0$ with
 a positive (resp. negative) sign if they are on the left (resp. right)
 side of $\mathcal{N}_0$ (\RefFig{fig:ds}).
-The directional scan is iterately processed from the start scan to both ends.
+The directional scan is iterately parsed from the start scan to both ends.
 At each iteration $i$, the scans $S_i$ and $S_{-i}$ are successively processed.
 
 \begin{figure}[h]
@@ -111,9 +111,11 @@ At each iteration $i$, the scans $S_i$ and $S_{-i}$ are successively processed.
      \put(-60,30){$\mathcal{N}_8$}
      \put(-169,8){$\mathcal{N}_{-5}$}
      \end{picture}
-  \caption{A directional scan: the start scan $S_0$ in blue, odd scans in
-           green, even scans in red, scan lines bounds $\mathcal{N}_i$ in
-           plain lines and scan strip bounds $\mathcal{D}$ in dotted lines.}
+  \caption{A directional scan.
+           The start scan $S_0$ is drawn in blue, odd scans in green,
+           even scans in red, the bounds of scan lines $\mathcal{N}_i$
+           with plain lines and the bounds of scan strip $\mathcal{D}$
+           with dotted lines.}
   \label{fig:ds}
 \end{figure}