Improve docstrings for captions

doc/examples/color_exposure/plot_ihc_color_separation.py

@@ -3,6 +3,8 @@
 Immunohistochemical staining colors separation
 ==============================================
 
+Color deconvolution consists in the separation of features by their colors.
+
 In this example we separate the immunohistochemical (IHC) staining from the
 hematoxylin counterstaining. The separation is achieved with the method
 described in [1]_, known as "color deconvolution".

doc/examples/edges/plot_contours.py

@@ -3,10 +3,10 @@
 Contour finding
 ===============
 
-``skimage.measure.find_contours`` uses a marching squares method to find
-constant valued contours in an image.  Array values are linearly interpolated
-to provide better precision of the output contours.  Contours which intersect
-the image edge are open; all others are closed.
+A marching squares method can be used to find constant valued contours in an
+image. Array values are linearly interpolated to provide better precision of
+the output contours. Contours which intersect the image edge are open; all
+others are closed.
 
 The `marching squares algorithm
 <http://www.essi.fr/~lingrand/MarchingCubes/algo.html>`__ is a special case of

doc/examples/edges/plot_line_hough_transform.py

@@ -3,12 +3,11 @@
 Straight line Hough transform
 =============================
 
-The Hough transform in its simplest form is a `method to detect straight lines
-<http://en.wikipedia.org/wiki/Hough_transform>`__.
+The Hough transform in its simplest form is a method to detect straight lines.
 
 In the following example, we construct an image with a line intersection. We
-then use the Hough transform to explore a parameter space for straight lines
-that may run through the image.
+then use the `Hough transform  <http://en.wikipedia.org/wiki/Hough_transform>`__.
+to explore a parameter space for straight lines that may run through the image.
 
 Algorithm overview
 ------------------

doc/examples/features_detection/plot_glcm.py

@@ -3,10 +3,9 @@
 GLCM Texture Features
 =====================
 
-This example illustrates texture classification using texture
-classification using grey level co-occurrence matrices (GLCMs).
-A GLCM is a histogram of co-occurring greyscale values at a given
-offset over an image.
+This example illustrates texture classification using grey level
+co-occurrence matrices (GLCMs). A GLCM is a histogram of co-occurring
+greyscale values at a given offset over an image.
 
 In this example, samples of two different textures are extracted from
 an image: grassy areas and sky areas. For each patch, a GLCM with

doc/examples/features_detection/plot_hog.py

@@ -3,12 +3,12 @@
 Histogram of Oriented Gradients
 ===============================
 
-The `Histogram of Oriented Gradient
-<http://en.wikipedia.org/wiki/Histogram_of_oriented_gradients>`__ (HOG) feature
-descriptor [1]_ is popular for object detection.
+The Histogram of Oriented Gradient (HOG) feature descriptor is popular
+for object detection [1]_.
 
-In the following example, we compute the HOG descriptor and display
-a visualisation.
+In the following example, we compute the `HOG descriptor
+<http://en.wikipedia.org/wiki/Histogram_of_oriented_gradients>`__
+and display a visualisation.
 
 Algorithm overview
 ------------------

doc/examples/features_detection/plot_template.py

@@ -3,8 +3,8 @@
 Template Matching
 =================
 
-In this example, we use template matching to identify the occurrence of an
-image patch (in this case, a sub-image centered on a single coin). Here, we
+We use template matching to identify the occurrence of an image patch
+(in this case, a sub-image centered on a single coin). Here, we
 return a single match (the exact same coin), so the maximum value in the
 ``match_template`` result corresponds to the coin location. The other coins
 look similar, and thus have local maxima; if you expect multiple matches, you