diff --git a/Article/Fig_method/algoAuto.tex b/Article/Fig_method/algoAuto.tex
index bafd70f2331b2c20c37f546820469531e3c5de70..8c25e21d3db37a694261074c90182bb4d766e1fc 100644
--- a/Article/Fig_method/algoAuto.tex
+++ b/Article/Fig_method/algoAuto.tex
@@ -24,21 +24,21 @@
\BlankLine
\bslist $\leftarrow$ \nullset\;
\mask $\leftarrow$ \nullset\;
- $i \leftarrow$ \resol/2\;
- \Repeat{$i <$ \iwidth/2}{
- \bslist $\leftarrow$ \bslist + \multi (\pt(\iwidth/2-i,\iheight),
- \pt(\iwidth/2-i,0), \mask)\;
- \bslist $\leftarrow$ \bslist + \multi (\pt(\iwidth/2+i,\iheight),
- \pt(\iwidth/2+i,0), \mask)\;
- $i \leftarrow i -$ \resol\;
+ $x \leftarrow$ \resol/2\;
+ \Repeat{$x <$ \iwidth/2}{
+ \bslist $\leftarrow$ \bslist + \multi (\pt(\iwidth/2-x,\iheight),
+ \pt(\iwidth/2-x,0), \mask)\;
+ \bslist $\leftarrow$ \bslist + \multi (\pt(\iwidth/2+x,\iheight),
+ \pt(\iwidth/2+x,0), \mask)\;
+ $x \leftarrow x~+$ \resol\;
}
- $i \leftarrow$ \resol/2\;
- \Repeat{$i <$ \iheight/2}{
- \bslist $\leftarrow$ \bslist + \multi (\pt(0,\iheight/2-i),
- \pt(\iwidth,\iheight/2-i), \mask)\;
- \bslist $\leftarrow$ \bslist + \multi (\pt(0,\iheight/2+i),
- \pt(\iwidth,\iheight/2+i), \mask)\;
- $i \leftarrow i -$ \resol\;
+ $y \leftarrow$ \resol/2\;
+ \Repeat{$y <$ \iheight/2}{
+ \bslist $\leftarrow$ \bslist + \multi (\pt(0,\iheight/2-y),
+ \pt(\iwidth,\iheight/2-y), \mask)\;
+ \bslist $\leftarrow$ \bslist + \multi (\pt(0,\iheight/2+y),
+ \pt(\iwidth,\iheight/2+y), \mask)\;
+ $y \leftarrow y~+$ \resol\;
}
\caption{AutoDetect: finds all blurred segments in the image.}
diff --git a/Article/Fig_method/algoMulti.tex b/Article/Fig_method/algoMulti.tex
index a0054750e01b797bc7da4b01db9259387ad42412..139655e9f39d4bcc9dd349e018c82b5fb3d7cb24 100644
--- a/Article/Fig_method/algoMulti.tex
+++ b/Article/Fig_method/algoMulti.tex
@@ -11,9 +11,9 @@
\SetKwData{ptb}{$B$}
\SetKwData{Result}{Result}
- \SetKwArray{occmask}{OccupancyMask}
+ \SetKwArray{mask}{$\mathcal{M}$}
\SetKwArray{bseg}{BlurredSegment}
- \SetKwArray{bslist}{ListOfBlurredSegments}
+ \SetKwArray{bslist}{ListOfBS}
\SetKwFunction{locmax}{ComputeAndSortGradientLocalMax}
\SetKwFunction{sortgradmax}{SortByGradientMagnitude}
@@ -24,20 +24,19 @@
\SetKwData{End}{End}
\Input{Stroke points \pta, \ptb}
+ \Input{Occupancy mask \mask}
\Output{\textit{\bslist} $\rightarrow$ list of detected blurred segments}
\BlankLine
\bslist $\leftarrow$ \nullset\;
- \occmask $\leftarrow$ \nullset\;
\lm $\leftarrow$ \locmax (\pta, \ptb)\;
\BlankLine
\For{$i \leftarrow 0$ \KwTo \taille(\lm)}{
- \bseg $\leftarrow$ detect (\lm[i], \ortho, \eps, \occmask)\;
+ \bseg $\leftarrow$ detect (\lm[i], \ortho, \eps, \mask)\;
\For{$j \leftarrow 0$ \KwTo \cardinal (\bseg)}{
- \occmask $\leftarrow$ \bseg[j]\;
+ \mask $\leftarrow$ \bseg[j]\;
}
\bslist $\leftarrow$ \bseg\;
}
- \caption{MultiDetection:
- finds all blurred segments under an input stroke.}
+ \caption{MultiDetect: finds all blurred segments from input selection.}
\end{algorithm}
diff --git a/Article/Fig_notions/escapeFirst_zoom.png b/Article/Fig_notions/escapeFirst_zoom.png
index a9038d5ccc3185c179f98e2f6a6432666bd88c95..283b80117b047d51d14981778746eb7d200d125c 100644
Binary files a/Article/Fig_notions/escapeFirst_zoom.png and b/Article/Fig_notions/escapeFirst_zoom.png differ
diff --git a/Article/Fig_notions/escapeSecond_full.png b/Article/Fig_notions/escapeSecond_full.png
new file mode 100644
index 0000000000000000000000000000000000000000..5107c1b7a372040b38b9d9622aa71b79fb4d44b0
Binary files /dev/null and b/Article/Fig_notions/escapeSecond_full.png differ
diff --git a/Article/Fig_notions/escapeSecond_zoom.png b/Article/Fig_notions/escapeSecond_zoom.png
new file mode 100644
index 0000000000000000000000000000000000000000..b4541372851b4d4c5db92d994ea376274e221fe5
Binary files /dev/null and b/Article/Fig_notions/escapeSecond_zoom.png differ
diff --git a/Article/Fig_notions/escapeThird_full.png b/Article/Fig_notions/escapeThird_full.png
new file mode 100644
index 0000000000000000000000000000000000000000..cb97e576ff7907be72fd7370ada1f18717a040fc
Binary files /dev/null and b/Article/Fig_notions/escapeThird_full.png differ
diff --git a/Article/Fig_notions/escapeThird_zoom.png b/Article/Fig_notions/escapeThird_zoom.png
new file mode 100644
index 0000000000000000000000000000000000000000..ff893c02524abe023b6fbb19649e390c08969ab8
Binary files /dev/null and b/Article/Fig_notions/escapeThird_zoom.png differ
diff --git a/Article/Fig_notions/scanstrip.eps b/Article/Fig_notions/scanstrip.eps
index eee7d6395e1964b3d022d54fc3f7638ae27f3301..2d0ff40a0d835dd46d680829b3f5c394a9601dd2 100644
--- a/Article/Fig_notions/scanstrip.eps
+++ b/Article/Fig_notions/scanstrip.eps
@@ -1,10 +1,10 @@
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%%Creator: cairo 1.14.6 (http://cairographics.org)
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+%%CreationDate: Mon Dec 17 22:48:00 2018
%%Pages: 1
%%DocumentData: Clean7Bit
%%LanguageLevel: 2
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+%%BoundingBox: 16 574 528 817
%%EndComments
%%BeginProlog
save
@@ -63,9 +63,9 @@ save
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diff --git a/Article/conclusion.tex b/Article/conclusion.tex
index 1d6642a4addba4dae9a2affc09805abb1c9bb4b6..8c76449b68ba577250c49e172f11851225e3ea8c 100755
--- a/Article/conclusion.tex
+++ b/Article/conclusion.tex
@@ -8,10 +8,10 @@ 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
\cite{KerautretEven09}.
-Despite of good performances obtained compared to existing detection methods
-found in the literature, the former approach suffers of two major drawbacks.
+Despite of good performances achieved, the former approach suffers of two
+major drawbacks.
It does not estimate the edge thickness so that many outliers are inserted
-into the blurred segment and the provided estmation of the edge orientation
+into the blurred segment and the provided estimation of the edge orientation
is biased.
Then the scan direction is derived from a bounded blurred segment, that
inevitably restricts its value to a finite set, so that long edges may be
diff --git a/Article/expe.tex b/Article/expe.tex
index 6b3606444d6ebb0716cfda37ca9c27fce443cf62..6da79de9c35f1ae9d2f13c142a33298f3d3e0bdf 100755
--- a/Article/expe.tex
+++ b/Article/expe.tex
@@ -85,7 +85,8 @@ 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.
-Moreover it lets some space to incorporate additional spurious outliers.
+Moreover it lets some space to incorporate additional spurious outliers,
+as illustrated in \RefFig{fig:outliers}.
With the new appoach, a real estimation of the edge width is provided.
The main risk of outlier incorporation remains at the beginning of the
blurred segment expansion as long as the minimal width continues to grow
diff --git a/Article/main.tex b/Article/main.tex
index 8981659ed04990248baa5b4253b3409439ac1801..f458d99b08eca7eff147248ce309fea8d4b787cf 100755
--- a/Article/main.tex
+++ b/Article/main.tex
@@ -4,6 +4,8 @@
\usepackage[T1]{fontenc}
\usepackage{graphicx}
+\usepackage{color}
+\definecolor{dwhite}{rgb}{1,1,1}
%\graphicspath{{./Fignotions/}{./Figmethod}}
\usepackage[ruled,vlined]{algorithm2e}
@@ -35,7 +37,7 @@
\begin{abstract}
\input{abstract}
- \keywords{Line detection \and discrete geometry \and ONE MORE PLEASE.}
+ \keywords{Line detection \and discrete objects \and image analysis}
\end{abstract}
\end{frontmatter}
diff --git a/Article/method.tex b/Article/method.tex
index f657dc10520b201be8d25725c77f414cbfe6c3cd..e46a0d7e43769fb579d3ff88ee836e7e5a84cefb 100755
--- a/Article/method.tex
+++ b/Article/method.tex
@@ -16,7 +16,7 @@ in the following figure.
\put(24,0){\framebox(56,30)}
\put(24,16){\makebox(56,10){Initial}}
\put(24,4){\makebox(56,10){detection}}
- \put(86,18){\scriptsize $\mathcal{B}_{1}$}
+ \put(86,18){\scriptsize $\mathcal{B}$}
\put(80,15){\vector(1,0){22}}
%\put(102,0){\framebox(56,30)}
\multiput(102,15)(28,9){2}{\line(3,-1){28}}
@@ -29,10 +29,10 @@ in the following figure.
\put(186,0){\framebox(56,30)}
\put(186,16){\makebox(56,10){Fine}}
\put(186,4){\makebox(60,10){tracking}}
- \put(250,18){\scriptsize $\mathcal{B}_2$}
+ \put(250,18){\scriptsize $\mathcal{B}'$}
\put(242,15){\vector(1,0){24}}
\put(266,0){\framebox(56,30){Filtering}}
- \put(330,18){\scriptsize $\mathcal{B}_3$}
+ \put(330,18){\scriptsize $\mathcal{B}''$}
\put(322,15){\vector(1,0){22}}
\end{picture}
\caption{The detection process main workflow.}
@@ -40,16 +40,17 @@ in the following figure.
\end{figure}
The initial detection consists in building and extending a blurred segment
-$\mathcal{B}_1$ based on the highest gradient points found in each scan
+$\mathcal{B}$ based on the highest gradient points found in each scan
of a static directional scanner based on an input segment $AB$.
-Validity tests based on the length or sparsity of $\mathcal{B}_1$ are
-applied to decide of the detection poursuit. In case of positive response,
-the position $C$ and direction $\vec{D}$ of this initial blurred segment
-are extracted.
+Validity tests are then applied to decide of the detection poursuit.
+They aim at rejecting a too short or too sparse blurred segment, or a
+blurred segment with a close orientation to the input segment $AB$.
+In case of positive response, the position $C$ and direction $\vec{D}$
+of this initial blurred segment are extracted.
The fine tracking step consists on building and extending a blurred segment
-$\mathcal{B}_2$ based on points that correspond to local maxima of the
+$\mathcal{B}'$ based on points that correspond to local maxima of the
image gradient, ranked by magnitude order, and with gradient direction
close to a reference gradient direction at the segment first point.
At this refinement step, the control of the assigned width is applied
@@ -58,7 +59,135 @@ direction $\vec{D}$ is used in order to extends the segment in the
appropriate direction.
The fine track output segment is finally filtered to remove artifacts
-and outliers, and a solution blurred segment $\mathcal{B}_3$ is provided.
+and outliers, and a final blurred segment $\mathcal{B}''$ is provided.
+
+\subsection{Adaptive directional scan}
+
+The blurred segment is searched within a directional scan with a position
+and an orientation approximately provided by the user, or blindly defined
+in unsupervised mode.
+Most of the time, the detection stops where the segment escapes sideways
+from the scan strip (\RefFig{fig:escape}).
+A second search is then run using another directional scan aligned
+on the detected segment.
+However, even in case of a correct detection, the estimated orientation
+of the segment is subject to the numerization rounding,
+and the longer the real segment to detect, the higher the probability to
+fail again on a blurred segment escape from the directional scan.
+
+\begin{figure}[h]
+\center
+ \begin{tabular}{c@{\hspace{0.2cm}}c@{\hspace{0.2cm}}c}
+ \includegraphics[width=0.31\textwidth]{Fig_notions/escapeFirst_zoom.png} &
+ \includegraphics[width=0.31\textwidth]{Fig_notions/escapeSecond_zoom.png} &
+ \includegraphics[width=0.31\textwidth]{Fig_notions/escapeThird_zoom.png}
+ \end{tabular}
+ \begin{picture}(1,1)(0,0)
+ \put(-314,-12){a)}
+ \put(-200,-12){b)}
+ \put(-86,-12){c)}
+ \end{picture}
+ \caption{Aborted detections on side escapes from the directional scan
+ during the initial tracking step (a) and during the fine tracking
+ step (b), and complete detection using an adaptive directional
+ scan (c). The input selection is drawn in red color, the scan
+ strip bounds in blue and the detected blurred segment in green.}
+ \label{fig:escape}
+\end{figure}
+
+%Even in ideal situation where the detected segment is a perfect line,
+%its width is never null as a result of the discretization process.
+%The estimated direction accuracy is mostly constrained by the length of
+%the detected segment.
+%To avoid these side escapes, the scan should not be a linear strip but
+%rather a conic shape to take into account the blurred segment preimage.
+%This side shift is amplified in situations where the blurred segment is
+%left free to get thicker in order to capture possible noisy features.
+%The assigned width is then still greater than the detected minimal width,
+%so that the segment can move within the directional scan.
+%Knowing the detected blurred segment shape and the image size, it is
+%possible to define a conic scan area, but this solution is computationaly
+%expensive because it leads to useless exploration of large image areas.
+%
+%\begin{figure}[h]
+%\center
+% %\begin{picture}(300,40)
+% %\end{picture}
+% \input{Fig_notions/bscone}
+% \caption{Possible extension area based
+% on the detected blurred segment preimage.}
+% \label{fig:cone}
+%\end{figure}
+
+To overcome this issue, in the former work, an additional refinement step is
+run using the better orientation estimated from the longer segment obtained.
+It is enough to completely detect most of the tested edges, but certainly
+not all, especially if larger images with much longer edges are processed.
+%The solution implemented in the former work was to let some arbitrary
+%margin between the scan strip width and the assigned width to the detection,
+%and to perform two fine detection steps, using for each of them the direction
+%found at the former step.
+As a solution, this operation could be itered as long as the blurred segment
+escapes from the directional scanner using as any fine detection steps as
+necessary.
+But at each iteration, already tested points are processed again,
+thus producing a useless computational cost.
+
+Here the proposed solution is to dynamically align the scan direction to
+the blurred segment one all along the expansion stage.
+At each iteration $i$, the scan strip is updated using the direction
+of the blurred segment computed at previous iteration $i-1$.
+The adaptive directional scan $ADS$ is then defined by :
+\begin{equation}
+%S_i = \mathcal{D}_{i-1} \cap \mathcal{N}_i
+ADS = \left\{
+S_i = \mathcal{D}_i \cap \mathcal{N}_i \cap \mathcal{I}
+\left| \begin{array}{l}
+\delta(\mathcal{N}_i) = - \delta^{-1}(\mathcal{D}_0) \\
+\wedge~ h_0(\mathcal{N}_i) = h_0(\mathcal{N}_{i-1}) + p(\mathcal{D}) \\
+\wedge~ \mathcal{D}_{i} = D (\mathcal{B}_{i-1},\varepsilon + k), i > 1
+\end{array} \right. \right\}
+\end{equation}
+where $D (\mathcal{B}_i,w)$ is the scan strip aligned to the
+detected segment at iteration $i$ with width $w$.
+In practice, the scan width is set a little greater than the assigned
+width $\varepsilon$ ($k$ is a constant arbitrarily set to 4).
+The last clause expresses the update of the scan bounds at iteration $i$.
+Compared to static directional scans, the scan strip moves while
+scan lines remain fixed.
+An example of adaptive directional scan is given in \RefFig{fig:adaption}.
+
+\begin{figure}[h]
+\center
+ \begin{tabular}{c@{\hspace{0.2cm}}c}
+ \includegraphics[width=0.49\textwidth]{Fig_notions/adaptionBounds_zoom.png}
+ & \includegraphics[width=0.49\textwidth]{Fig_notions/adaptionLines_zoom.png}
+ \end{tabular}
+ \caption{Example of blurred segment detection
+ using an adaptive directional scan.
+ On the right picture, the scan bounds are displayed in red, the
+ detected blurred segment in blue, and its bounding lines in green.
+ The left picture displays the successive scans.
+ Here the adaption is visible at the crossing of the tile joins.}
+ \label{fig:adaption}
+\end{figure}
+
+\subsection{Control of the assigned width}
+
+The assigned width $\varepsilon$ to the blurred segment recognition algorithm
+is initially set to a large value $\varepsilon_0$ in order to allow the
+detection of large blurred segments.
+Then, when no more augmentation of the minimal width is observed after
+$\lambda$ iterations ($\mu_{i+\lambda} = \mu_i$), it is set to a much
+stricter value able to circumscribe the possible interpretations of the
+segment, that take into account the digitization margins:
+\begin{equation}
+\varepsilon = \mu_{i+\lambda} + 1/2
+\end{equation}
+This strategy aims at preventing the incorporation of spurious outliers in
+further parts of the segment.
+Setting the observation distance to a constant value $\lambda = 20$ seems
+appropriate in most experimented situations.
\subsection{Supervised blurred segment detection}
@@ -69,7 +198,7 @@ dragging and the output blurred segment is displayed on-the-fly.
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,
+In order to temper this undesirable behavior for particular applications,
the initial detection can be optionally run twice, the second fast scan being
aligned on the first detection output.
This strategy provides a first quick analysis of the local context before
@@ -86,7 +215,7 @@ 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) 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
+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.
@@ -96,7 +225,8 @@ blurred segment extension in the fine tracking step.
Beyond the possible detection of a large set of edges at once, the
multi-detection allows the detection of some unaccessible edges in
classical single detection mode. This is particularly the case of edges
-that are quite close to a more salient edge with a higher gradient.
+that are quite close to a more salient edge with a higher gradient,
+as illustrated in \RefFig{fig:voisins}.
The multi-detection detects both edges and the user may then select
the awaited one.
@@ -112,11 +242,11 @@ the awaited one.
\parbox{0.22\textwidth}{\centering{\scriptsize{c)}}} &
\parbox{0.22\textwidth}{\centering{\scriptsize{d)}}}
\end{tabular}
- \caption{Example of edge disclosed by the multi-detection mode:
- a) the input selection on the intensity image,
- b) the gradient map,
- c) the only sharper edge detected in classical single mode,
- d) a neighbouring edge disclosed by the multi-detection mode. }
+ \caption{Detection of close edges with different sharpness:
+ a) input selection across the edges,
+ b) gradient map,
+ c) in single mode, detection of only the edge with the higher gradient,
+ d) in multi-detection mode, detection of both edges. }
\label{fig:voisins}
\end{figure}
@@ -151,7 +281,7 @@ 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),
-both edges (in different colours) are highlighted.
+both edges (in different colors) are highlighted.
These two thin blurred segments are much shorter, probably because the
tiles are not perfectly aligned.
This example illustrates the versatility of the new detector.
@@ -166,7 +296,7 @@ to collect all the segments found under the stroke.
\input{Fig_method/algoAuto}
-The behaviour of the unsupervised detection is depicted through the two
+The behavior of the unsupervised detection is depicted through the two
examples of \RefFig{fig:auto}.
The example on the left shows the detection of thin straight objects on a
circle with variable width.
@@ -181,7 +311,7 @@ are grouped to form a thick segment.
The example on the right shows the limits of the edge detector on a picture
with quite dense repartition of gradient.
-All the salient edges are well detected but they are surrounded be a lot
+All the salient edges are well detected but they are surrounded by a lot
of false detections, that rely on the presence of many local maxima of
the gradient magnitude with similar orientations.
@@ -194,8 +324,3 @@ the gradient magnitude with similar orientations.
\caption{Automatic detection of blurred segments.}
\label{fig:auto}
\end{figure}
-
-% \subsection{Implementation details}
-%
-% A directional scanner is encoded as an iterator that provides successively
-% all the scan lines.
diff --git a/Article/notions.tex b/Article/notions.tex
index 50ffd1317b6ff47ba80070e1ab094c7df9ab73b9..21c395ce7c88b920405be44b68dd9c7c2cc8f41b 100755
--- a/Article/notions.tex
+++ b/Article/notions.tex
@@ -38,7 +38,8 @@ arithmetical width of the narrowest digital straight line that contains
$\mathcal{B}$.
It is also the minimal width of the convex hull of $\mathcal{B}$,
that can be computed by Melkman's algorithm \cite{Melkman87}.
-The extension of the blurred segment $\mathcal{B}_{i-1}$ of assigned width
+As depicted on \RefFig{fig:bs},
+the extension of the blurred segment $\mathcal{B}_{i-1}$ of assigned width
$\varepsilon$ and minimal width $\mu_{i-1}$ at step $i-1$ with a new input
point $P_i$ is thus controlled by the recognition test $\mu_i < \varepsilon$.
@@ -59,9 +60,11 @@ domain $\mathcal{I}$ of a digital straight line $\mathcal{D}$,
called the {\it scan strip}, into scans $S_i$, each of them being a segment
of a naive line $\mathcal{N}_i$ orthogonal to $\mathcal{D}$.
\begin{equation}
-DS = \left\{ S_i = \mathcal{D} \cap \mathcal{N}_i \cap \mathcal{I} |
-\delta(\mathcal{N}_i) = - \delta^{-1}(\mathcal{D})
-\cap h_0(\mathcal{N}_i) = h_0(\mathcal{N}_{i-1}) + p(\mathcal{D}) \right\}
+DS = \left\{ S_i = \mathcal{D} \cap \mathcal{N}_i \cap \mathcal{I}
+\left| \begin{array}{l}
+\delta(\mathcal{N}_i) = - \delta^{-1}(\mathcal{D}) \\
+\wedge~ h_0(\mathcal{N}_i) = h_0(\mathcal{N}_{i-1}) + p(\mathcal{D})
+\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
@@ -74,8 +77,10 @@ time.
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 $\mathcal{N}_0$.
+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.
+At each iteration $i$, the scans $S_i$ and $S_{-i}$ are successively processed.
\begin{figure}[h]
\center
@@ -83,7 +88,28 @@ side of $\mathcal{N}_0$.
\includegraphics[width=0.8\textwidth]{Fig_notions/scanstrip.eps}
\begin{picture}(1,1)(0,0)
\thicklines
- \put(-173,112){\vector(2,-1){30}}
+ \put(-176,112){\vector(2,-1){30}}
+ \put(-90,19){\vector(-2,1){30}}
+ {\color{dwhite}{
+ \put(-181,114.5){\circle*{10}}
+ \put(-84,16.5){\circle*{10}}
+ \put(-16,102.5){\circle*{10}}
+ \put(-132,66.5){\circle*{12}}
+ \put(-72,96.5){\circle*{12}}
+ \put(-175.5,65.5){\circle*{20}}
+ \put(-117,10.5){\circle*{14}}
+ \put(-54,32.5){\circle*{14}}
+ \put(-161,10.5){\circle*{20}}
+ }}
+ \put(-185,112){$A$}
+ \put(-88,14){$B$}
+ \put(-20,98){$\mathcal{D}$}
+ \put(-137,64){$S_0$}
+ \put(-77,94){$S_8$}
+ \put(-183,64){$S_{-5}$}
+ \put(-123,8){$\mathcal{N}_0$}
+ \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
@@ -95,17 +121,17 @@ A directional scan can be defined by its start scan $S_0$.
If $A(x_A,y_A)$ and $B(x_B,y_B)$ are the end points of $S_0$,
the scan strip is defined by :
\begin{equation}
-\mathcal{D}(A,B) = \mathcal{L}(\delta_x, \delta_y, min (c1,c2), 1 + |c_1-c_2|)
+\mathcal{D}(A,B) =
+\mathcal{L}(\delta_x,~ \delta_y,~ min (c1,c2),~ 1 + |c_1-c_2|)
\end{equation}
\noindent
where $\delta_x = x_B - x_A$, $\delta_y = y_B - y_A$,
$c_1 = \delta_x\cdot x_A + \delta_y\cdot y_A$ and
$c_2 = \delta_x\cdot x_B + \delta_y\cdot y_B$.
-
The scan line $\mathcal{N}_i$ is then defined by :
\begin{equation}
-\mathcal{N}_i(A,B) = \mathcal{L}(\delta_y, -\delta_x,
-\delta_y\cdot x_A - \delta_x\cdot y_A + i\cdot \nu_{AB}, \nu_{AB})
+\mathcal{N}_i(A,B) = \mathcal{L}(\delta_y,~ -\delta_x,~
+\delta_y\cdot x_A - \delta_x\cdot y_A + i\cdot \nu_{AB},~ \nu_{AB})
\end{equation}
where $\nu_{AB} = max (|\delta_x|, |\delta_y|)$
@@ -115,132 +141,15 @@ where $\nu_{AB} = max (|\delta_x|, |\delta_y|)$
%as the image bounds should also be processed anyway.
A directional scan can also be defined by its central point $C(x_C,y_C)$,
-its direction $\vec{D}(x_D,y_D)$ and its width $w$. The scan strip is :
+its direction $\vec{D}(X_D,Y_D)$ and its width $w$. The scan strip is :
\begin{equation}
\mathcal{D}(C,\vec{D},w)
-= \mathcal{L}(y_D, -x_D, x_C\cdot y_D - y_C\cdot x_D - w / 2, w)
+= \mathcal{L}(Y_D,~ -X_D,~ x_C\cdot Y_D - y_C\cdot X_D - w / 2,~ w)
\end{equation}
\noindent
and the scan line $\mathcal{N}_i(C,\vec{D},w)$ :
\begin{equation}
-\mathcal{N}_i(C,\vec{D},w) = \mathcal{L}(x_D, y_D,
- x_D\cdot x_C + y_D\cdot y_C - w / 2 + i\cdot w, max (|x_D|,|y_D|)
-\end{equation}
-
-\subsection{Adaptive directional scan}
-
-The blurred segment is searched within a directional scan with a position
-and an orientation approximately provided by the user, or blindly defined
-in unsupervised mode.
-Most of the time, the detection stops where the segment escapes sideways
-from the scan strip (\RefFig{fig:escape}).
-A second search is then run using another directional scan aligned
-on the detected segment.
-However, even in the case of an exact detection, the estimated orientation
-of the segment is subject to the numerization rounding,
-and the longer the real segment to detect, the higher the probability to
-fail again on a blurred segment escape from the directional scan.
-
-\begin{figure}[h]
-\center
- \begin{tabular}{c}
- \includegraphics[width=0.49\textwidth]{Fig_notions/escapeFirst_zoom.png}
- \end{tabular}
- \caption{Example of early detection failures
- on side escapes from the directional scan.}
- \label{fig:escape}
-\end{figure}
-
-%Even in ideal situation where the detected segment is a perfect line,
-%its width is never null as a result of the discretization process.
-%The estimated direction accuracy is mostly constrained by the length of
-%the detected segment.
-%To avoid these side escapes, the scan should not be a linear strip but
-%rather a conic shape to take into account the blurred segment preimage.
-%This side shift is amplified in situations where the blurred segment is
-%left free to get thicker in order to capture possible noisy features.
-%The assigned width is then still greater than the detected minimal width,
-%so that the segment can move within the directional scan.
-%Knowing the detected blurred segment shape and the image size, it is
-%possible to define a conic scan area, but this solution is computationaly
-%expensive because it leads to useless exploration of large image areas.
-%
-%\begin{figure}[h]
-%\center
-% %\begin{picture}(300,40)
-% %\end{picture}
-% \input{Fig_notions/bscone}
-% \caption{Possible extension area based
-% on the detected blurred segment preimage.}
-% \label{fig:cone}
-%\end{figure}
-
-In the former work, an additional refinement step is run using the better
-orientation estimated from the longer segment obtained.
-It is enough to completely detect most of the tested edges, but certainly
-not all, especially if larger images with much longer edges are processed.
-%The solution implemented in the former work was to let some arbitrary
-%margin between the scan strip width and the assigned width to the detection,
-%and to perform two fine detection steps, using for each of them the direction
-%found at the former step.
-This operation could be itered as long as the blurred segment escapes from
-the directional scanner using as any fine detection steps as necessary.
-But at each iteration, already tested points are processed again,
-thus producing a useless computational cost.
-
-Here the proposed solution is to dynamically align the scan direction to
-the blurred segment one all along the expansion stage.
-At each iteration $i+1$, the scan strip is updated using the direction
-of the blurred segment computed at previous iteration $i$.
-The adaptive directional scan $ADS$ is then defined by :
-\begin{equation}
-%S_i = \mathcal{D}_{i-1} \cap \mathcal{N}_i
-ADS = \left\{
-S_i = \mathcal{D}_i \cap \mathcal{N}_i \cap \mathcal{I}
-\left| \begin{array}{l}
-\delta(\mathcal{N}_i) = - \delta^{-1}(\mathcal{D}_0) \\
-\cap h_0(\mathcal{N}_i) = h_0(\mathcal{N}_{i-1}) + p(\mathcal{D}) \\
-\cap \mathcal{D}_{i+1} = D (\mathcal{B}_i,\varepsilon + k)
-\end{array} \right. \right\}
-\end{equation}
-where $D (\mathcal{B}_i,w)$ is the scan strip aligned to the
-detected segment at iteration $i$ with width $w$.
-In practice, the scan width is set a little greater than the assigned
-width $\varepsilon$ ($k$ is a constant arbitrarily set to 4).
-The last clause expresses the update of the scan bounds at iteration $i+1$.
-Compared to static directional scans, the scan strip moves while
-scan lines remain fixed.
-An example of adaptive directional scan is given in \RefFig{fig:adaption}.
-
-\begin{figure}[h]
-\center
- \begin{tabular}{c@{\hspace{0.2cm}}c}
- \includegraphics[width=0.49\textwidth]{Fig_notions/adaptionBounds_zoom.png}
- & \includegraphics[width=0.49\textwidth]{Fig_notions/adaptionLines_zoom.png}
- \end{tabular}
- \caption{Example of blurred segment detection
- using an adaptive directional scan.
- On the right picture, the scan bounds are displayed in red, the
- detected blurred segment in blue, and its bounding lines in green.
- The left picture displays the successive scans.
- Adaption is quite sensible when crossing the tile joins.}
- \label{fig:adaption}
-\end{figure}
-
-\subsection{Control of the assigned width}
-
-The assigned width $\varepsilon$ to the blurred segment recognition algorithm
-is initially set to a large value $\varepsilon_0$ in order to allow the
-detection of large blurred segments.
-Then, when no more augmentation of the minimal width is observed after
-$\lambda$ iterations ($\mu_{i+\lambda} = \mu_i$), it is set to a much
-stricter value able to circumscribe the possible interpretations of the
-segment, that take into account the digitization margins:
-\begin{equation}
-\varepsilon = \mu_{i+\lambda} + 1/2
+\mathcal{N}_i(C,\vec{D},w) = \mathcal{L}(X_D,~ Y_D,~
+ X_D\cdot x_C + Y_D\cdot y_C - w / 2 + i\cdot w,~ max (|X_D|,|Y_D|)
\end{equation}
-This strategy aims at preventing the incorporation of spurious outliers in
-further parts of the segment.
-Setting the observation distance to a constant value $\lambda = 20$ seems
-appropriate in most experimented situations.
diff --git a/Code/Seg/BSTools/bsdetectionwidget.cpp b/Code/Seg/BSTools/bsdetectionwidget.cpp
index 85b7745ae39074417de82e54340a3c8560d89ba7..026c152cbeca8b83f561c854579cbc4bdc5173eb 100755
--- a/Code/Seg/BSTools/bsdetectionwidget.cpp
+++ b/Code/Seg/BSTools/bsdetectionwidget.cpp
@@ -43,6 +43,7 @@ BSDetectionWidget::BSDetectionWidget (QWidget *parent)
verbose = false;
background = BACK_IMAGE;
bsBoundsVisible = false;
+ blevel = 0;
// Sets display parameters
selectionColor = Qt::red;
@@ -703,6 +704,13 @@ void BSDetectionWidget::keyPressEvent (QKeyEvent *event)
<< (bsPointsVisible ? "visible" : "hidden") << endl;
displayDetectionResult ();
}
+ else
+ {
+ // Tunes the background image black level
+ incBlackLevel ((event->modifiers () & Qt::ShiftModifier) ? -1 : 1);
+ displayDetectionResult ();
+ cout << "Background black level = " << getBlackLevel () << endl;
+ }
break;
case Qt::Key_Z :
@@ -720,7 +728,7 @@ void BSDetectionWidget::keyPressEvent (QKeyEvent *event)
{
// Tunes the thickenning limit
detector.incThickenningLimit (
- (event->modifiers () & Qt::ShiftModifier ? -1 : 1));
+ (event->modifiers () & Qt::ShiftModifier) ? -1 : 1);
extract ();
cout << "Thickenning limit = " << detector.getThickenningLimit ()
<< " pixels" << endl;
@@ -858,6 +866,29 @@ void BSDetectionWidget::drawBlurredSegment (QPainter &painter,
}
+void BSDetectionWidget::incBlackLevel (int val)
+{
+ blevel += val * 5;
+ if (blevel < 0) blevel = 0;
+ if (blevel > 200) blevel = 200;
+}
+
+
+void BSDetectionWidget::lighten (QImage &im)
+{
+ if (blevel != 0 && background != BACK_BLACK && background != BACK_WHITE)
+ {
+ for (int i = 0; i < im.height (); i++)
+ for(int j = 0; j < im.width (); j++)
+ {
+ int col = blevel + (QColor (im.pixel(j,i)).value ()
+ * (255 - blevel)) / 255;
+ im.setPixel (j, i, col + col * 256 + col * 256 * 256);
+ }
+ }
+}
+
+
void BSDetectionWidget::displayBackground ()
{
if (background == BACK_BLACK) augmentedImage.fill (qRgb (0, 0, 0));
@@ -897,6 +928,7 @@ void BSDetectionWidget::displayDetectionResult ()
else if (background == BACK_WHITE) augmentedImage.fill (qRgb (255, 255, 255));
else if (background == BACK_IMAGE) augmentedImage = loadedImage;
else augmentedImage = gradImage;
+ lighten (augmentedImage);
QPainter painter (&augmentedImage);
vector<BlurredSegment *> bss = detector.getBlurredSegments ();
if (! bss.empty ())
@@ -1017,6 +1049,9 @@ void BSDetectionWidget::writeDetectionStatus ()
else if (res == BSDetector::RESULT_INITIAL_TOO_MANY_OUTLIERS)
cout << "Extraction : unsuccessful filter test at initial detection."
<< endl;
+ else if (res == BSDetector::RESULT_INITIAL_CLOSE_ORIENTATION)
+ cout << "Extraction : initial detection of a too closely oriented segment."
+ << endl;
else if (res == BSDetector::RESULT_FINAL_NO_DETECTION)
cout << "Extraction : no final detection (bsini == NULL)." << endl;
else if (res == BSDetector::RESULT_FINAL_TOO_FEW)
diff --git a/Code/Seg/BSTools/bsdetectionwidget.h b/Code/Seg/BSTools/bsdetectionwidget.h
index a961972c142787e331b412c49e90271c5bb4e071..fe937fdb3e53903486d33dea30b58fab04339a1c 100755
--- a/Code/Seg/BSTools/bsdetectionwidget.h
+++ b/Code/Seg/BSTools/bsdetectionwidget.h
@@ -230,6 +230,8 @@ private:
/** Background type.
* BACK_BLACK, BACK_WHITE, BACK_IMAGE, BACK_GRAD, BACK_GRADX, BACK_GRADY. */
int background;
+ /** Black level used to lighten background images. */
+ int blevel;
/** Flag indicating whether detection result should be output. */
bool verbose;
@@ -315,6 +317,23 @@ private:
void drawBlurredSegment (QPainter &painter,
BlurredSegment *bs, bool high = true);
+ /**
+ * \brief Returns the background black level.
+ */
+ inline int getBlackLevel () const { return (blevel); }
+
+ /**
+ * \brief Increments the background black level.
+ * @param val Increment value;
+ */
+ void incBlackLevel (int val);
+
+ /**
+ * \brief Lighten the image according to the black level set.
+ * @param im Image to lighten.
+ */
+ void lighten (QImage &im);
+
/**
* \brief Displays the window background (no detection).
*/
diff --git a/Code/Seg/BSTools/bsstructureitem.cpp b/Code/Seg/BSTools/bsstructureitem.cpp
index 77bc856b50688abcfc227c707e4fc6e218a61063..ce96bb346905f998befd50ea296c93aab985fc37 100755
--- a/Code/Seg/BSTools/bsstructureitem.cpp
+++ b/Code/Seg/BSTools/bsstructureitem.cpp
@@ -36,6 +36,7 @@ BSStructureItem::BSStructureItem (int width, int height, const QImage *im,
det = detector;
displayItem = DISPLAY_MIN;
displayScanLines = false;
+ displayInput = false;
this->im = im;
verbose = true;
infoPen = QPen (Qt::red, DEFAULT_PEN_WIDTH, Qt::SolidLine,
@@ -274,6 +275,17 @@ void BSStructureItem::paintScansAndFilter (QPainter *painter, int step)
}
}
+ // Display the input selection
+ if (displayInput)
+ {
+ vector<Pt2i> stroke;
+ Pt2i ip1, ip2, ipc;
+ int iw;
+ det->getScanInput (BSDetector::STEP_INITIAL, ip1, ip2, iw, ipc);
+ ip1.draw (stroke, ip2);
+ paintPixels (painter, stroke, Qt::red);
+ }
+
// Displays filter output
if (det->isFiltering (step))
{
@@ -282,6 +294,13 @@ void BSStructureItem::paintScansAndFilter (QPainter *painter, int step)
BlurredSegment *bs = det->getBlurredSegment (step);
if (bs != NULL) paintPixels (painter, bs->getStartPt (), Qt::yellow);
}
+
+ // Displays the blurred segment
+ else
+ {
+ BlurredSegment *bs = det->getBlurredSegment (step);
+ if (bs != NULL) paintPixels (painter, bs->getAllPoints (), Qt::green);
+ }
}
if (verbose)
diff --git a/Code/Seg/BSTools/bsstructureitem.h b/Code/Seg/BSTools/bsstructureitem.h
index 4b2d66036731eeef39a4fcc1584c3b9029cc447c..d1e8e809809ed3ddfcf3309e064ecc082ceeabd3 100755
--- a/Code/Seg/BSTools/bsstructureitem.h
+++ b/Code/Seg/BSTools/bsstructureitem.h
@@ -76,10 +76,14 @@ public:
/**
* \brief Switches on or off the scan line display modality.
- * @param next Get next information if true, previous on otherwise.
*/
inline void switchScanDisplay () { displayScanLines = ! displayScanLines; }
+ /**
+ * \brief Switches on or off the input selection display modality.
+ */
+ inline void switchInputDisplay () { displayInput = ! displayInput; }
+
/**
* \brief Returns the displayed information title.
*/
@@ -168,6 +172,8 @@ private:
int displayItem;
/** Scan display modality. */
bool displayScanLines;
+ /** Input selection display modality. */
+ bool displayInput;
/** Information text modality. */
bool verbose;
/** Information text style. */
diff --git a/Code/Seg/BSTools/bsstructureview.cpp b/Code/Seg/BSTools/bsstructureview.cpp
index e40b0f11ed683cfebf6cd2915abaf2cff4eb9d84..606444f06c591878aad00ff185990507ed019e48 100755
--- a/Code/Seg/BSTools/bsstructureview.cpp
+++ b/Code/Seg/BSTools/bsstructureview.cpp
@@ -97,6 +97,14 @@ bool BSStructureView::processKeyEvent (QKeyEvent *event)
processed = false;
break;
+ case Qt::Key_J : // Input selection display
+ if (event->modifiers () & Qt::ControlModifier)
+ grid->switchInputDisplay ();
+ scene()->update ();
+ update ();
+ processed = false;
+ break;
+
case Qt::Key_V : // Info display
grid->switchInfoDisplay ();
scene()->update ();
@@ -120,7 +128,7 @@ bool BSStructureView::processKeyEvent (QKeyEvent *event)
// QRect (QPoint (0, 0),
// QSize (grid->getWidth(), grid->getHeight()))
// ).toImage().save ("structure.png");
- // cout << "Structure shot in capture.png" << endl;
+ // cout << "Structure shot in structure.png" << endl;
break;
case Qt::Key_Plus : // Zoom in
diff --git a/Code/Seg/BlurredSegment/bsdetector.cpp b/Code/Seg/BlurredSegment/bsdetector.cpp
index b988af8ba1a5d9308f9566c4bf2c1f7855c3f334..53138985d9ada74f6e98a2b229a894d549ccd7c5 100755
--- a/Code/Seg/BlurredSegment/bsdetector.cpp
+++ b/Code/Seg/BlurredSegment/bsdetector.cpp
@@ -14,6 +14,7 @@ const int BSDetector::RESULT_INITIAL_NO_DETECTION = 11;
const int BSDetector::RESULT_INITIAL_TOO_FEW = 12;
const int BSDetector::RESULT_INITIAL_TOO_SPARSE = 13;
const int BSDetector::RESULT_INITIAL_TOO_MANY_OUTLIERS = 14;
+const int BSDetector::RESULT_INITIAL_CLOSE_ORIENTATION = 15;
const int BSDetector::RESULT_FINAL_NO_DETECTION = 21;
const int BSDetector::RESULT_FINAL_TOO_FEW = 22;
const int BSDetector::RESULT_FINAL_TOO_SPARSE = 23;
@@ -114,6 +115,51 @@ void BSDetector::detectAll ()
}
+void BSDetector::detectAllWithBalancedXY ()
+{
+ autodet = true;
+ freeMultiSelection ();
+ gMap->setMasking (true);
+
+ bool isnext = true;
+ nbtrials = 0;
+ int width = gMap->getWidth ();
+ int height = gMap->getHeight ();
+ int xg = width / 2, yb = height / 2;
+ int xd = xg + autoResol, yh = yb + autoResol;
+ bool agauche = true, enbas = true, adroite = true, enhaut = true;
+ while (isnext && (agauche || enbas || adroite || enhaut))
+ {
+ if (agauche)
+ {
+ isnext = runMultiDetection (Pt2i (xg, 0), Pt2i (xg, height - 1));
+ xg -= autoResol;
+ if (xg <= 0) agauche = false;
+ }
+ if (isnext && enbas)
+ {
+ isnext = runMultiDetection (Pt2i (0, yb), Pt2i (width - 1, yb));
+ yb -= autoResol;
+ if (yb <= 0) enbas = false;
+ }
+ if (isnext && adroite)
+ {
+ isnext = runMultiDetection (Pt2i (xd, 0), Pt2i (xd, height - 1));
+ xd += autoResol;
+ if (xd >= width - 1) adroite = false;
+ }
+ if (isnext && enhaut)
+ {
+ isnext = runMultiDetection (Pt2i (0, yh), Pt2i (width - 1, yh));
+ yh += autoResol;
+ if (yh >= height - 1) enhaut = false;
+ }
+ }
+ gMap->clearMask ();
+ gMap->setMasking (false);
+}
+
+
void BSDetector::detectSelection (const Pt2i &p1, const Pt2i &p2)
{
autodet = false;
@@ -219,6 +265,15 @@ void BSDetector::olddetect (const Pt2i &p1, const Pt2i &p2,
}
*/
+ // Orientation test for automatic extractions
+ //-------------------------------------------
+ Vr2i bsinidir = bsini->getSupportVector();
+ if (bsinidir.orientedAs (inip1.vectorTo (inip2)))
+ {
+ resultValue = RESULT_INITIAL_CLOSE_ORIENTATION;
+ return;
+ }
+
// Gradient reference selection
//-----------------------------
Pt2i pCenter = bsini->getCenter ();
@@ -234,8 +289,7 @@ void BSDetector::olddetect (const Pt2i &p1, const Pt2i &p2,
// Finer detection based on gradient maxima with orientation constraint
//---------------------------------------------------------------------
- bsf = bstold->fineTrack (pCenter, bsini->getSupportVector(),
- scanwidth, bswidth, gRef);
+ bsf = bstold->fineTrack (pCenter, bsinidir, scanwidth, bswidth, gRef);
if (bsf == NULL || bsf->size () < bsMinSize)
{
resultValue = (bsf == NULL ? RESULT_FINAL_NO_DETECTION
@@ -379,6 +433,15 @@ void BSDetector::detect (const Pt2i &p1, const Pt2i &p2,
return;
}
+ // Orientation test for automatic extractions
+ //-------------------------------------------
+ Vr2i bsinidir = bsini->getSupportVector();
+ if (bsinidir.orientedAs (inip1.vectorTo (inip2)))
+ {
+ resultValue = RESULT_INITIAL_CLOSE_ORIENTATION;
+ return;
+ }
+
// Gradient reference selection
//-----------------------------
Pt2i pCenter = bsini->getCenter ();
@@ -400,8 +463,7 @@ void BSDetector::detect (const Pt2i &p1, const Pt2i &p2,
// Finer detection based on gradient maxima with orientation constraint
//---------------------------------------------------------------------
- bsf = bst2->fineTrack (pCenter, bsini->getSupportVector(),
- scanwidth, bswidth, gRef);
+ bsf = bst2->fineTrack (pCenter, bsinidir, scanwidth, bswidth, gRef);
if (bsf == NULL || bsf->size () < bsMinSize)
{
resultValue = (bsf == NULL ? RESULT_FINAL_NO_DETECTION
diff --git a/Code/Seg/BlurredSegment/bsdetector.h b/Code/Seg/BlurredSegment/bsdetector.h
index 7d4f93f7a32495680f76bc7c6d2ebdc704ec5c3d..8944d975c8692ca45fad1c20faa95ae975c6aefc 100755
--- a/Code/Seg/BlurredSegment/bsdetector.h
+++ b/Code/Seg/BlurredSegment/bsdetector.h
@@ -40,6 +40,8 @@ public:
static const int RESULT_INITIAL_TOO_SPARSE;
/** Extraction result : unsuccessful filter test at initial detection. */
static const int RESULT_INITIAL_TOO_MANY_OUTLIERS;
+ /** Extraction result : initial detection of a closely oriented segment. */
+ static const int RESULT_INITIAL_CLOSE_ORIENTATION;
/** Extraction result : no final detection (bsf == NULL). */
static const int RESULT_FINAL_NO_DETECTION;
/** Extraction result : too few points at final detection. */
@@ -72,9 +74,16 @@ public:
/**
* \brief Detects all blurred segments in the picture.
+ * Parses X direction first, the Y direction.
*/
void detectAll ();
+ /**
+ * \brief Detects all blurred segments in the picture.
+ * Parses simultaneously the X and Y directions.
+ */
+ void detectAllWithBalancedXY ();
+
/**
* \brief Detects blurred segments between two input points.
* @param p1 First input point.
diff --git a/Code/Seg/BlurredSegment/bstracker.cpp b/Code/Seg/BlurredSegment/bstracker.cpp
index 6ac9c1c22cfcd90ce0ce472e9fb6fc8141f89f42..4462ab317df41e2374ffb29eb40f1f82e2df83a9 100755
--- a/Code/Seg/BlurredSegment/bstracker.cpp
+++ b/Code/Seg/BlurredSegment/bstracker.cpp
@@ -209,7 +209,7 @@ BlurredSegment *BSTracker::fineTrack (const Pt2i ¢er, const Vr2i &scandir,
// Gets detected segment normal vector
Vr2i normal = scandir.orthog ();
- if (! normal.orientedAs (gref)) normal.invert ();
+ if (! normal.directedAs (gref)) normal.invert ();
fail = 0;
diff --git a/Code/Seg/ImageTools/vmap.cpp b/Code/Seg/ImageTools/vmap.cpp
index 142b3b5a64522eb17d8d8ddaf53dc07ca7700243..a4b85a29d85d6ce1b2d9483a7189497ea77acec0 100755
--- a/Code/Seg/ImageTools/vmap.cpp
+++ b/Code/Seg/ImageTools/vmap.cpp
@@ -534,8 +534,8 @@ int VMap::keepContrastedMax (int *lmax, int n, int *in) const
}
-int VMap::keepOrientedElementsIn (const vector<Pt2i> &pix,
- const Vr2i &ref, int n, int *ind) const
+int VMap::keepDirectedElementsAs (const vector<Pt2i> &pix,
+ int n, int *ind, const Vr2i &ref) const
{
int vx = ref.x ();
int vy = ref.y ();
@@ -550,8 +550,8 @@ int VMap::keepOrientedElementsIn (const vector<Pt2i> &pix,
}
-int VMap::keepDirectedElementsIn (const vector<Pt2i> &pix,
- const Vr2i &ref, int n, int *ind) const
+int VMap::keepOrientedElementsAs (const vector<Pt2i> &pix,
+ int n, int *ind, const Vr2i &ref) const
{
int vx = ref.x ();
int vy = ref.y ();
@@ -613,10 +613,10 @@ int VMap::localMax (int *lmax, const vector<Pt2i> &pix, const Vr2i &gref) const
// Prunes the candidates with opposite gradient
if (orientedGradient)
- count = keepOrientedElementsIn (pix, gref, count, lmax);
+ count = keepDirectedElementsAs (pix, count, lmax, gref);
// Prunes the candidates wrongly oriented
- count = keepDirectedElementsIn (pix, gref, count, lmax);
+ count = keepOrientedElementsAs (pix, count, lmax, gref);
// Sorts candidates by gradient magnitude
sortMax (lmax, count, gn);
diff --git a/Code/Seg/ImageTools/vmap.h b/Code/Seg/ImageTools/vmap.h
index 4c43e13fb0ef67940964b171e4e09709848941db..6e4d4829e36558286e6404ca683890073142abec 100755
--- a/Code/Seg/ImageTools/vmap.h
+++ b/Code/Seg/ImageTools/vmap.h
@@ -164,28 +164,29 @@ public:
int keepContrastedMax (int *lmax, int n, int *in) const;
/**
- * Keeps elements that are oriented along a reference vector
- * in a selection of points.
+ * Keeps elements with the same direction as a reference vector
+ * in a selection of points (positive scalar product).
* Returns the number of remaining elements in the selection.
* @param pix Input array of scanned points.
- * @param ref The reference vector.
* @param n Initial size of the selection of points.
* @param ind Selection of points.
+ * @param ref The reference vector.
*/
- int keepOrientedElementsIn (const vector<Pt2i> &pix,
- const Vr2i &ref, int n, int *ind) const;
+ int keepDirectedElementsAs (const vector<Pt2i> &pix,
+ int n, int *ind, const Vr2i &ref) const;
/**
* Keeps elements with gradient value near a reference vector
* in a selection of points.
+ * Relies on angleThreshold value for the test.
* Returns the number of remaining elements in the selection.
* @param pix Input array of scanned points.
- * @param ref The reference vector.
* @param n Initial size of the selection of points.
* @param ind Selection of points.
+ * @param ref The reference vector.
*/
- int keepDirectedElementsIn (const vector<Pt2i> &pix,
- const Vr2i &ref, int n, int *ind) const;
+ int keepOrientedElementsAs (const vector<Pt2i> &pix,
+ int n, int *ind, const Vr2i &ref) const;
/**
* \brief Gets filtered and sorted local gradient maxima.
diff --git a/Code/Seg/ImageTools/vr2i.cpp b/Code/Seg/ImageTools/vr2i.cpp
index 5f0ab4d8eb15a7bf94a377bba71a1b4a7dca688a..32f3398b84c01abdf50ea1865fdee31ff54eb61f 100755
--- a/Code/Seg/ImageTools/vr2i.cpp
+++ b/Code/Seg/ImageTools/vr2i.cpp
@@ -28,6 +28,14 @@ Vr2i Vr2i::orthog () const
}
+bool Vr2i::orientedAs (const Vr2i &ref) const
+{
+ int ps = xv * ref.xv + yv * ref.yv;
+ return (4 * ps * ps > 3 * (xv * xv + yv * yv)
+ * (ref.xv * ref.xv + ref.yv * ref.yv));
+}
+
+
bool *Vr2i::steps (int *n) const
{
int x2 = (xv > 0 ? xv : - xv);
diff --git a/Code/Seg/ImageTools/vr2i.h b/Code/Seg/ImageTools/vr2i.h
index 35cca7ab2135e9cf3a3b6c4df8c9febb380c2d83..cac1af7dd1b85a151609f2aeb215935d66e96409 100755
--- a/Code/Seg/ImageTools/vr2i.h
+++ b/Code/Seg/ImageTools/vr2i.h
@@ -121,11 +121,19 @@ public:
inline void setOrthog () { int tmp = xv; xv = - yv; yv = tmp; }
/**
- * @fn bool orientedAs (Vr2i v)
- * \brief Returns true if v has the same orientation as the vector.
+ * @fn bool directedAs (Vr2i ref)
+ * \brief Returns true if v has the same direction as a reference.
+ * @param ref The reference vector.
*/
- inline bool orientedAs (Vr2i v) const {
- return (xv * v.xv + yv * v.yv >= 0); }
+ inline bool directedAs (const Vr2i &ref) const {
+ return (xv * ref.xv + yv * ref.yv >= 0); }
+
+ /**
+ * @fn bool orientedAs (Vr2i ref)
+ * \brief Returns true if v has the same orientation as a reference.
+ * @param ref The reference vector.
+ */
+ bool orientedAs (const Vr2i &ref) const;
/**
* @fn void invert ()
diff --git a/Methode/ctrl.tex b/Methode/ctrl.tex
index 8510a56d1ec03e2f35b43ef36759b4ae7a8a3bd9..0c66a0e10f65cf38cd1cd7e5bfab021167cd1dce 100755
--- a/Methode/ctrl.tex
+++ b/Methode/ctrl.tex
@@ -27,7 +27,6 @@ a/q/c &\spa & Enregistre / Affiche / Supprime les segments \\
b && Ajuste la r\'esolution du gradient pour le filtrage des max locaux \\
e && Inverse la direction de r\'ef\'erence (autre bord) \\
g && Ajuste le seuil du gradient \\
-n && Allume le segment suivant dans une d\'etection multiple \\
j && Ajuste le seuil de voisinage pour les suivis rapides \\
k && Ajuste la taille minimale des composantes connexes \\
l && Ajuste la taille minimale du segment final \\
@@ -40,6 +39,7 @@ s && Ajuste la longueur tol\'er\'ee pour les sauts de d\'etection \\
u && Relance la d\'etection sur la derni\`ere s\'election (update) \\
w && Ajuste la consigne d'\'epaisseur du segment flou pour le suivi fin \\
x && Ajuste la consigne d'\'epaisseur du segment flou pour le suivi rapide \\
+y && Ajuste le contraste de l'image \\
z && Ajuste le seuil du contr\^ole de la consigne d'\'epaisseur \\
Ctrl-b && Commute le fond d'\'ecran de la fen\^etre principale \\
Ctrl-d && Commute le test de densit\'e \\
@@ -83,6 +83,7 @@ p && Capture la fen\^etre dans un fichier .png \\
\hline \hline
\multicolumn{3}{|l|}{Analyse des segments flous extraits :} \\
Ctrl-i && Commute le type d'affichage des scans \\
+Ctrl-j && Commute l'affichage de la barre de s\'election \\
Ctrl-b && Commute le fond d'\'ecran de la fen\^etre principale. \\
+ -- && Zoom \\
$< \wedge > \vee$ && D\'ecalage de l'observation \\
diff --git a/Methode/methode.tex b/Methode/methode.tex
index 9fccb59171a5c946dbe9fc557bd177df88c72745..230285a9ca2bb1821fd0fa46979d9ce2db016132 100755
--- a/Methode/methode.tex
+++ b/Methode/methode.tex
@@ -16,7 +16,7 @@
\section*{S\'equencement de la d\'etection}
-\begin{picture}(150,160)(0,0)
+\begin{picture}(150,170)(0,0)
\put(40,0){\framebox(70,10){FILTRAGE}}
\put(75,20){\vector(0,-1){10}}
\put(40,20){\framebox(70,10){TEST DE CONNEXITE}}
@@ -28,17 +28,19 @@
\put(75,60){\vector(0,-1){10}}
\put(40,60){\framebox(70,10){RECENTRAGE}}
\put(75,80){\vector(0,-1){10}}
-\put(40,80){\framebox(70,10){PRE-FILTRAGE}}
+\put(40,80){\framebox(70,10){TEST D'ORIENTATION}}
\put(75,100){\vector(0,-1){10}}
-\put(40,100){\framebox(70,10){TEST DE DENSITE}}
+\put(40,100){\framebox(70,10){PRE-FILTRAGE}}
\put(75,120){\vector(0,-1){10}}
+\put(40,120){\framebox(70,10){TEST DE DENSITE}}
+\put(75,140){\vector(0,-1){10}}
\thicklines
-\put(40,120){\framebox(70,10){\bf \large SUIVI RAPIDE}}
-\put(42,122){\framebox(66,6)}
+\put(40,140){\framebox(70,10){\bf \large SUIVI RAPIDE}}
+\put(42,142){\framebox(66,6)}
\thinlines
-\put(75,140){\vector(0,-1){10}}
+\put(75,160){\vector(0,-1){10}}
\thicklines
-\put(40,140){\framebox(70,10){\bf \large SUIVI PRELIMINAIRE}}
+\put(40,160){\framebox(70,10){\bf \large SUIVI PRELIMINAIRE}}
\thinlines
\end{picture}
@@ -126,6 +128,17 @@ isol\'es \`a l'\'ecart du segment.
\end{itemize}
\end{itemize}
+\subsection*{Test d'orientation}
+
+\begin{itemize}
+\item Etat par d\'efaut : activ\'e.
+\item Fonctionnement :
+\begin{itemize}
+\item V\'erifie que l'orientation du segment n'est pas trop proche de
+celle de la barre de balayage $P_1P_2$ ($30^o$).
+\end{itemize}
+\end{itemize}
+
\subsection*{Recentrage}
\begin{itemize}