diff --git a/.travis.yml b/.travis.yml
index f50ba1ac..3a127d3d 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -1,7 +1,7 @@
 # vim ft=yaml
 # travis-ci.org definition for skimage build
 #
-# We pretend to be erlang because we need can't use the python support in
+# We pretend to be erlang because we can't use the python support in
 # travis-ci; it uses virtualenvs, they do not have numpy, scipy, matplotlib,
 # and it is impractical to build them
 
diff --git a/doc/examples/plot_hough_transform.py b/doc/examples/plot_hough_transform.py
index b74132da..b15df92e 100644
--- a/doc/examples/plot_hough_transform.py
+++ b/doc/examples/plot_hough_transform.py
@@ -59,7 +59,7 @@ References
 
 '''
 
-from skimage.transform import hough, hough_peaks, probabilistic_hough
+from skimage.transform import hough_line, hough_peaks, probabilistic_hough
 from skimage.filter import canny
 from skimage import data
 
@@ -77,7 +77,7 @@ idx = np.arange(25, 75)
 image[idx[::-1], idx] = 255
 image[idx, idx] = 255
 
-h, theta, d = hough(image)
+h, theta, d = hough_line(image)
 
 plt.figure(figsize=(8, 4))
 
diff --git a/doc/source/plots/hough_tf.py b/doc/source/plots/hough_tf.py
index 8b745864..ea2fc4bb 100644
--- a/doc/source/plots/hough_tf.py
+++ b/doc/source/plots/hough_tf.py
@@ -1,7 +1,7 @@
 import numpy as np
 import matplotlib.pyplot as plt
 
-from skimage.transform import hough
+from skimage.transform import hough_line
 
 img = np.zeros((100, 150), dtype=bool)
 img[30, :] = 1
@@ -11,7 +11,7 @@ for i in range(90):
     img[i, i] = 1
 img += np.random.random(img.shape) > 0.95
 
-out, angles, d = hough(img)
+out, angles, d = hough_line(img)
 
 plt.subplot(1, 2, 1)
 
@@ -20,8 +20,8 @@ plt.title('Input image')
 
 plt.subplot(1, 2, 2)
 plt.imshow(out, cmap=plt.cm.bone,
-           extent=(d[0], d[-1],
-                   np.rad2deg(angles[0]), np.rad2deg(angles[-1])))
+           extent=(np.rad2deg(angles[0]), np.rad2deg(angles[-1]),
+                   d[0], d[-1]))
 plt.title('Hough transform')
 plt.xlabel('Angle (degree)')
 plt.ylabel('Distance (pixel)')
diff --git a/skimage/draw/_draw.pyx b/skimage/draw/_draw.pyx
index 4316cadc..ee2b89e0 100644
--- a/skimage/draw/_draw.pyx
+++ b/skimage/draw/_draw.pyx
@@ -293,12 +293,12 @@ def ellipse_perimeter(Py_ssize_t cy, Py_ssize_t cx, Py_ssize_t yradius,
     cy, cx : int
         Centre coordinate of ellipse.
     yradius, xradius: int
-        Main radial values.
+        Minor and major semi-axes. ``(x/xradius)**2 + (y/yradius)**2 = 1``.
 
     Returns
     -------
     rr, cc : (N,) ndarray of int
-        Indices of pixels that belong to the circle perimeter.
+        Indices of pixels that belong to the ellipse perimeter.
         May be used to directly index into an array, e.g.
         ``img[rr, cc] = 1``.
 
diff --git a/skimage/transform/hough_transform.py b/skimage/transform/hough_transform.py
index e83cecd5..f112a025 100644
--- a/skimage/transform/hough_transform.py
+++ b/skimage/transform/hough_transform.py
@@ -124,6 +124,12 @@ def hough_line(img, theta=None):
     distances : ndarray
         Distance values.
 
+    Notes
+    -----
+    The origin is the top left corner of the original image.
+    The angle is counted clockwise from 9 o'clock.
+    The distance is the minimal algebraic distance from this origin to the line.
+
     Examples
     --------
     Generate a test image:
@@ -138,7 +144,7 @@ def hough_line(img, theta=None):
 
     Apply the Hough transform:
 
-    >>> out, angles, d = hough(img)
+    >>> out, angles, d = hough_line(img)
 
     .. plot:: hough_tf.py
 
@@ -178,12 +184,12 @@ def hough_peaks(hspace, angles, dists, min_distance=10, min_angle=10,
     Parameters
     ----------
     hspace : (N, M) array
-        Hough space returned by the `hough` function.
+        Hough space returned by the `hough_line` function.
     angles : (M,) array
-        Angles returned by the `hough` function. Assumed to be continuous
+        Angles returned by the `hough_line` function. Assumed to be continuous
         (`angles[-1] - angles[0] == PI`).
     dists : (N, ) array
-        Distances returned by the `hough` function.
+        Distances returned by the `hough_line` function.
     min_distance : int
         Minimum distance separating lines (maximum filter size for first
         dimension of hough space).
@@ -204,14 +210,14 @@ def hough_peaks(hspace, angles, dists, min_distance=10, min_angle=10,
     Examples
     --------
     >>> import numpy as np
-    >>> from skimage.transform import hough, hough_peaks
+    >>> from skimage.transform import hough_line, hough_peaks
     >>> from skimage.draw import line
     >>> img = np.zeros((15, 15), dtype=np.bool_)
     >>> rr, cc = line(0, 0, 14, 14)
     >>> img[rr, cc] = 1
     >>> rr, cc = line(0, 14, 14, 0)
     >>> img[cc, rr] = 1
-    >>> hspace, angles, dists = hough(img)
+    >>> hspace, angles, dists = hough_line(img)
     >>> hspace, angles, dists = hough_peaks(hspace, angles, dists)
     >>> angles
     array([  0.74590887,  -0.79856126])