diff --git a/doc/examples/plot_fill_holes.py b/doc/examples/plot_fill_holes.py
new file mode 100644
index 00000000..7257d2bb
--- /dev/null
+++ b/doc/examples/plot_fill_holes.py
@@ -0,0 +1,70 @@
+"""
+==========
+Fill holes
+==========
+
+In this example, we fill holes (i.e. isolated, dark spots) in an image using
+morphological reconstruction by erosion. Erosion expands the minimal values of
+the seed image until it encounters a mask image. Thus, the seed image and mask
+image represent the maximum and minimum possible values of the reconstructed
+image.
+
+We start with an image containing both peaks and holes:
+"""
+import matplotlib.pyplot as plt
+
+from skimage import data
+from skimage.exposure import rescale_intensity
+
+image = data.moon()
+# Rescale image intensity so that we can see dim features.
+image = rescale_intensity(image, in_range=(50, 200))
+
+# convenience function for plotting images
+def imshow(image, **kwargs):
+    plt.figure(figsize=(5, 4))
+    plt.imshow(image, **kwargs)
+    plt.axis('off')
+
+imshow(image)
+plt.title('original image')
+
+"""
+.. image:: PLOT2RST.current_figure
+
+Now we need to create the seed image, where the minima represent the starting
+points for erosion.  To fill holes, we initialize the seed image to the maximum
+value of the original image. Along the borders, however, we use the original
+values of the image. These border pixels will be the starting points for the
+erosion process. We then limit the erosion by setting the mask to the values
+of the original image.
+"""
+
+import numpy as np
+from skimage.morphology import reconstruction
+
+seed = np.copy(image)
+seed[1:-1, 1:-1] = image.max()
+mask = image
+
+filled = reconstruction(seed, mask, method='erosion')
+
+imshow(filled, vmin=image.min(), vmax=image.max())
+plt.title('after filling holes')
+
+"""
+.. image:: PLOT2RST.current_figure
+
+As shown above, eroding inward from the edges removes holes, since (by
+definition) holes are surrounded by pixels of brighter value. Finally, we can
+isolate the dark regions by subtracting the reconstructed image from the
+original image.
+"""
+
+imshow(image - filled)
+plt.title('dark holes')
+plt.show()
+
+"""
+.. image:: PLOT2RST.current_figure
+"""
diff --git a/doc/examples/plot_find_spots.py b/doc/examples/plot_find_spots.py
new file mode 100644
index 00000000..ff6e2196
--- /dev/null
+++ b/doc/examples/plot_find_spots.py
@@ -0,0 +1,69 @@
+"""
+==========
+Find spots
+==========
+
+In this example, we find bright spots in an image using morphological
+reconstruction by dilation. Dilation expands the maximal values of the seed
+image until it encounters a mask image. Thus, the seed image and mask image
+represent the minimum and maximum possible values of the reconstructed image.
+
+We start with an image containing both peaks and holes:
+"""
+import matplotlib.pyplot as plt
+
+from skimage import data
+from skimage.exposure import rescale_intensity
+
+image = data.moon()
+# Rescale image intensity so that we can see dim features.
+image = rescale_intensity(image, in_range=(50, 200))
+
+# convenience function for plotting images
+def imshow(image, **kwargs):
+    plt.figure(figsize=(5, 4))
+    plt.imshow(image, **kwargs)
+    plt.axis('off')
+
+imshow(image)
+plt.title('original image')
+
+"""
+.. image:: PLOT2RST.current_figure
+
+Now we need to create the seed image, where the maxima represent the starting
+points for dilation. To find spots, we initialize the seed image to the minimum
+value of the original image. Along the borders, however, we use the original
+values of the image. These border pixels will be the starting points for the
+dilation process. We then limit the dilation by setting the mask to the values
+of the original image.
+"""
+
+import numpy as np
+from skimage.morphology import reconstruction
+
+seed = np.copy(image)
+seed[1:-1, 1:-1] = image.min()
+mask = image
+
+rec = reconstruction(seed, mask, method='dilation')
+
+imshow(rec, vmin=image.min(), vmax=image.max())
+plt.title('')
+
+"""
+.. image:: PLOT2RST.current_figure
+
+As shown above, dilating inward from the edges removes peaks, since (by
+definition) peaks are surrounded by pixels of darker value. Finally, we can
+isolate the bright spots by subtracting the reconstructed image from the
+original image.
+"""
+
+imshow(image - rec)
+plt.title('"holes"')
+plt.show()
+
+"""
+.. image:: PLOT2RST.current_figure
+"""