diff --git a/doc/examples/plot_brief.py b/doc/examples/plot_brief.py
index 390da1e5..526c4acd 100644
--- a/doc/examples/plot_brief.py
+++ b/doc/examples/plot_brief.py
@@ -18,13 +18,11 @@ the BRIEF method and provides rotation and scale-invariance, see
 `skimage.feature.ORB`.
 
 """
-import numpy as np
 from skimage import data
 from skimage import transform as tf
 from skimage.feature import (match_descriptors, corner_peaks, corner_harris,
                              plot_matches, BRIEF)
 from skimage.color import rgb2gray
-from skimage import img_as_float
 import matplotlib.pyplot as plt
 
 
diff --git a/doc/examples/plot_censure.py b/doc/examples/plot_censure.py
new file mode 100644
index 00000000..468b07d2
--- /dev/null
+++ b/doc/examples/plot_censure.py
@@ -0,0 +1,41 @@
+"""
+========================
+CenSurE feature detector
+========================
+
+The CenSurE feature detector is a scale-invariant center-surround detectors
+(CenSurE) that claims to outperform other detectors and is capable of real-time
+implementation.
+
+"""
+from skimage import data
+from skimage import transform as tf
+from skimage.feature import CenSurE
+from skimage.color import rgb2gray
+import matplotlib.pyplot as plt
+
+
+img1 = rgb2gray(data.lena())
+tform = tf.AffineTransform(scale=(1.5, 1.5), rotation=0.5,
+                           translation=(150, -200))
+img2 = tf.warp(img1, tform)
+
+detector = CenSurE()
+keypoints1, scales1 = detector.detect(img1)
+keypoints2, scales2 = detector.detect(img2)
+
+fig, ax = plt.subplots(nrows=1, ncols=2)
+
+plt.gray()
+
+ax[0].imshow(img1)
+ax[0].axis('off')
+ax[0].scatter(keypoints1[:, 1], keypoints1[:, 0], 2 ** scales1,
+              facecolors='none', edgecolors='r')
+
+ax[1].imshow(img2)
+ax[1].axis('off')
+ax[1].scatter(keypoints2[:, 1], keypoints2[:, 0], 2 ** scales2,
+              facecolors='none', edgecolors='r')
+
+plt.show()
diff --git a/doc/examples/plot_orb.py b/doc/examples/plot_orb.py
index d8b5cfbf..534c4634 100644
--- a/doc/examples/plot_orb.py
+++ b/doc/examples/plot_orb.py
@@ -12,13 +12,11 @@ allows to employ the very efficient Hamming distance metric for matching and
 is thus preferred for real-time applications.
 
 """
-import numpy as np
 from skimage import data
 from skimage import transform as tf
 from skimage.feature import (match_descriptors, corner_harris,
                              corner_peaks, ORB, plot_matches)
 from skimage.color import rgb2gray
-from skimage import img_as_float
 import matplotlib.pyplot as plt