diff --git a/CONTRIBUTORS.txt b/CONTRIBUTORS.txt
index 80cb92f0..fd0656db 100644
--- a/CONTRIBUTORS.txt
+++ b/CONTRIBUTORS.txt
@@ -132,7 +132,7 @@
 
 - François Boulogne
   Andres Method for circle perimeter, ellipse perimeter drawing.
-  Hough transform for circles
+  Circular Hough Transform
 
 - Thouis Jones
   Vectorized operators for arrays of 16-bit ints.
diff --git a/doc/examples/plot_circular_hough_transform.py b/doc/examples/plot_circular_hough_transform.py
index 0caee3c4..cc4ea664 100755
--- a/doc/examples/plot_circular_hough_transform.py
+++ b/doc/examples/plot_circular_hough_transform.py
@@ -1,14 +1,32 @@
-#!/usr/bin/env python2
-# -*- coding: utf-8 -*-
-# Author: Francois Boulogne
-# License: GPL
-
 """
 ========================
 Circular Hough Transform
 ========================
 
+The Hough transform in its simplest form is a `method to detect
+straight lines <http://en.wikipedia.org/wiki/Hough_transform>`__
+but it can also be used to detect circles.
 
+In the following example, the Hough transform is used to detect
+coin positions and match their edges. We provide a range of
+plausible radii. For each radius, two circles are extracted and
+we finally keep the five most prominent candidates.
+The result shows that coin positions are well-detected.
+
+
+Algorithm overview
+------------------
+
+Given a black circle on a white background, we first guess its
+radius (or a range of radii) to construct a new circle.
+This circle is applied on each black pixel of the original picture
+and the coordinates of this circle are voting in an accumulator.
+From this geometrical construction, the original circle center
+position receives the highest score.
+
+Note that the accumulator size is built to be larger than the
+original picture in order to detect centers outside the frame.
+Its size is extended by two times the larger radius.
 
 """
 
@@ -23,23 +41,32 @@ from skimage.feature import peak_local_max
 
 # Load picture and detect edges
 image = data.coins()[0:95, 70:370]
-edges = filter.canny(filter.sobel(image), sigma=2.8)
-
+edges = filter.canny(image, sigma=3, low_threshold=10, high_threshold=50)
 
 fig, ax = plt.subplots(ncols=1, nrows=1, figsize=(6, 6))
 ax.imshow(image, cmap=plt.cm.gray)
 
 # Detect two radii
-radii = np.array([21, 25])
-hough_res = hough_circle(edges, radii)
+hough_radii = np.arange(15, 30, 2)
+hough_res = hough_circle(edges, hough_radii)
 
-for radius, h in zip(radii, hough_res):
-    # For each radius, keep two circles
-    maxima = peak_local_max(h, num_peaks=2)
-    for maximum in maxima:
-        center_x, center_y = maximum - radii.max()
-        circ = mpatches.Circle((center_y, center_x), radius,
-                               fill=False, edgecolor='red', linewidth=2)
-        ax.add_patch(circ)
+centers = []
+accums = []
+radii = []
+
+for radius, h in zip(hough_radii, hough_res):
+    # For each radius, extract two circles
+    peaks = peak_local_max(h, num_peaks=2)
+    centers.extend(peaks - hough_radii.max())
+    accums.extend(h[peaks[:, 0], peaks[:, 1]])
+    radii.extend([radius, radius])
+
+# Draw the most prominent 5 circles
+for idx in np.argsort(accums)[::-1][:5]:
+    center_x, center_y = centers[idx]
+    radius = radii[idx]
+    circ = mpatches.Circle((center_y, center_x), radius,
+                           fill=False, edgecolor='red', linewidth=2)
+    ax.add_patch(circ)
 
 plt.show()