Article: american wanted

Article/method.tex

@@ -90,7 +90,7 @@ fail again on a blurred segment escape from the directional scan.
   \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 colour, the scan
+           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}
@@ -198,7 +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
@@ -281,7 +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.
@@ -296,7 +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.
@@ -311,7 +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.