Minor changes in wording.

doc/examples/applications/plot_coins_segmentation.py

@@ -9,8 +9,8 @@ several coins outlined against a darker background. The segmentation of
 the coins cannot be done directly from the histogram of grey values,
 because the background shares enough grey levels with the coins that a
 thresholding segmentation is not sufficient. Simply thresholding the image 
-leads either to missing significant parts of the coins, or to getting parts 
-of the background together with the coins.
+leads either to missing significant parts of the coins, or to merging parts
+of the background with the coins.
 
 We first try an edge-based segmentation. We use the Canny detector to 
 delineate the contours of the coins. These contours are filled using 

doc/source/user_guide/segmentation.txt

@@ -23,7 +23,7 @@ thresholding segmentation is not sufficient.
     >>> histo = np.histogram(coins, bins=np.arange(0, 256))
 
 Simply thresholding the image leads either to missing significant parts
-of the coins, or to getting parts of the background together with the
+of the coins, or to merging parts of the background with the
 coins. This is due to the inhomogeneous lighting of the image. 
 
 .. image:: ../../_images/plot_coins_segmentation_2.png
@@ -36,7 +36,7 @@ use the gradients rather than the grey values.
 Edge-based segmentation
 ~~~~~~~~~~~~~~~~~~~~~~~
 
-Let us first try to detect edges than enclose the coins. For edge
+Let us first try to detect edges that enclose the coins. For edge
 detection, we use the `Canny detector 
 <http://en.wikipedia.org/wiki/Canny_edge_detector>`_ of ``skimage.filter.canny``
 
@@ -49,7 +49,7 @@ As the background is very smooth, almost all edges are found at the
 boundary of the coins, or inside the coins.
 Now that we have contours that delineate the outer boundary of the coins,
 we fill the inner part of the coins using the
-``ndimage.binary_fill_holes`` function, that uses mathematical morphology
+``ndimage.binary_fill_holes`` function, which uses mathematical morphology
 to fill the holes.
 
 ::
@@ -63,7 +63,7 @@ to fill the holes.
 
 Most coins are well segmented out of the background. Small objects from
 the background can be easily removed using the ``ndimage.label``
-function, and removing objects smaller than a small threshold.
+function to remove objects smaller than a small threshold.
 
 ::
 
@@ -80,15 +80,15 @@ the filling function did not fill the inner part of the coin.
 
 Therefore, this segmentation method is not very robust: if we miss a
 single pixel of the contour of the object, we will not be able to fill
-it. Of course, we could try first to dilate the contours in order to
+it. Of course, we could try to dilate the contours in order to
 close them. However, it is preferable to try a more robust method.
 
 Region-based segmentation
 ~~~~~~~~~~~~~~~~~~~~~~~~~
 
 Let us first determine markers of the coins and the background. These
-markers are pixels that we can label unambiguously with one of the
-phases. Here, the markers are found at the two extreme parts of the
+markers are pixels that we can label unambiguously as either object or
+background. Here, the markers are found at the two extreme parts of the
 histogram of grey values:
 
 ::
@@ -100,19 +100,19 @@ histogram of grey values:
 We will use these markers in a watershed segmentation. The name watershed
 comes from an analogy with hydrology. The `watershed transform
 <http://en.wikipedia.org/wiki/Watershed_%28image_processing%29>`_ floods
-an image of elevation from markers, in order to determine the catchment
+an image of elevation starting from markers, in order to determine the catchment
 basins of these markers. Watershed lines separate these catchment basins,
-and correspond to the segmentation that is looked for.
+and correspond to the desired segmentation.
 
-The choice of the elevation map is critical for a good segmentation.
+The choice of the elevation map is critical for good segmentation.
 Here, the amplitude of the gradient provides a good elevation map. We
 use the Sobel operator for computing the amplitude of the gradient::
 
     >>> from skimage.filter import sobel
     >>> elevation_map = sobel(coins)
 
-From the 3-D surface plot shown below, we see that high barriers separate
-well the coins from the background.
+From the 3-D surface plot shown below, we see that high barriers effectively
+separate the coins from the background.
 
 .. image:: elevation_map.jpg
     :align: center