diff --git a/doc/examples/color_exposure/plot_ihc_color_separation.py b/doc/examples/color_exposure/plot_ihc_color_separation.py index 2623355c..23b52cb1 100644 --- a/doc/examples/color_exposure/plot_ihc_color_separation.py +++ b/doc/examples/color_exposure/plot_ihc_color_separation.py @@ -3,6 +3,8 @@ Immunohistochemical staining colors separation ============================================== +Color deconvolution consists of 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". diff --git a/doc/examples/edges/plot_contours.py b/doc/examples/edges/plot_contours.py index e686b4ac..a9eddd60 100644 --- a/doc/examples/edges/plot_contours.py +++ b/doc/examples/edges/plot_contours.py @@ -3,9 +3,9 @@ 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 +We use a marching squares method to find constant valued contours in an image. +In ``skimage.measure.find_contours``, 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 diff --git a/doc/examples/edges/plot_line_hough_transform.py b/doc/examples/edges/plot_line_hough_transform.py index 06f23203..e013efb1 100644 --- a/doc/examples/edges/plot_line_hough_transform.py +++ b/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 -`__. +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 `__. +to explore a parameter space for straight lines that may run through the image. Algorithm overview ------------------ diff --git a/doc/examples/features_detection/plot_glcm.py b/doc/examples/features_detection/plot_glcm.py index a5d17c82..47b17432 100644 --- a/doc/examples/features_detection/plot_glcm.py +++ b/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 diff --git a/doc/examples/features_detection/plot_hog.py b/doc/examples/features_detection/plot_hog.py index e7b524cb..e3c89b63 100644 --- a/doc/examples/features_detection/plot_hog.py +++ b/doc/examples/features_detection/plot_hog.py @@ -3,12 +3,12 @@ Histogram of Oriented Gradients =============================== -The `Histogram of Oriented Gradient -`__ (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 +`__ +and display a visualisation. Algorithm overview ------------------ diff --git a/doc/examples/features_detection/plot_template.py b/doc/examples/features_detection/plot_template.py index c7b4f01a..abf97b4b 100644 --- a/doc/examples/features_detection/plot_template.py +++ b/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