From c6f16dbff5c42ca35993c52f9380a9706b7b4a9b Mon Sep 17 00:00:00 2001
From: Nelle Varoquaux <nelle.varoquaux@gmail.com>
Date: Wed, 21 Dec 2011 19:09:40 +0100
Subject: [PATCH 1/7] Harris corner detector

---
 doc/examples/plot_harris.py |  30 +++++++++++
 skimage/filter/__init__.py  |   1 +
 skimage/filter/harris.py    | 103 ++++++++++++++++++++++++++++++++++++
 3 files changed, 134 insertions(+)
 create mode 100644 doc/examples/plot_harris.py
 create mode 100644 skimage/filter/harris.py

diff --git a/doc/examples/plot_harris.py b/doc/examples/plot_harris.py
new file mode 100644
index 00000000..258925ed
--- /dev/null
+++ b/doc/examples/plot_harris.py
@@ -0,0 +1,30 @@
+"""
+===============================================================================
+Harris Corner detector
+===============================================================================
+
+The Harris corner filter detects interest points using edge detection in many
+direction.
+"""
+from matplotlib import pyplot as plt
+from matplotlib import cm
+
+from skimage import data
+from skimage.filter import harris
+
+
+def plot_harris_points(image, filtered_coords):
+    """ plots corners found in image"""
+
+    plt.subplot(111)
+    plt.imshow(image, cmap=cm.gray)
+    plt.plot([p[1] for p in filtered_coords],
+                [p[0] for p in filtered_coords],
+                'b.')
+    plt.axis('off')
+    plt.show()
+
+
+im = data.lena().astype(float)
+filtered_coords = harris.harris_corner_detector(im, 6)
+plot_harris_points(im, filtered_coords)
diff --git a/skimage/filter/__init__.py b/skimage/filter/__init__.py
index 1acc33f2..6075291f 100644
--- a/skimage/filter/__init__.py
+++ b/skimage/filter/__init__.py
@@ -5,3 +5,4 @@ from edges import sobel, hsobel, vsobel, hprewitt, vprewitt, prewitt
 from tv_denoise import tv_denoise
 from rank_order import rank_order
 from thresholding import threshold_otsu
+from harris import harris_corner_detector
diff --git a/skimage/filter/harris.py b/skimage/filter/harris.py
new file mode 100644
index 00000000..53b58b01
--- /dev/null
+++ b/skimage/filter/harris.py
@@ -0,0 +1,103 @@
+#
+# Harris detector
+#
+# http://www.janeriksolem.net/2009/01/harris-corner-detector-in-python.html
+
+import numpy as np
+from scipy import ndimage
+
+
+def _compute_harris_response(image, eps=1e-6):
+    """Compute the Harris corner detector response function
+        for each pixel in the image
+
+    Params
+    -------
+    image: ndarray
+
+    eps: float, optional, default: 1e-6
+    normalisation factor
+
+    Returns
+    --------
+    ndarray
+    """
+    if len(image.shape) == 3:
+        image = image.mean(axis=2)
+
+    # derivatives
+    image = ndimage.gaussian_filter(image, 1)
+    imx = ndimage.sobel(image, axis=0, mode='constant')
+    imy = ndimage.sobel(image, axis=1, mode='constant')
+
+    Wxx = ndimage.gaussian_filter(imx * imx, 1.5, mode='constant')
+    Wxy = ndimage.gaussian_filter(imx * imy, 1.5, mode='constant')
+    Wyy = ndimage.gaussian_filter(imy * imy, 1.5, mode='constant')
+
+    # determinant and trace
+    Wdet = Wxx * Wyy - Wxy ** 2
+    Wtr = Wxx + Wyy
+    harris = Wdet / (Wtr + eps)
+
+    # Non maximum filter of size 3
+    harris_max = ndimage.maximum_filter(harris, 3, mode='constant')
+    harris *= harris == harris_max
+    # Remove the image corners
+    harris[:3] = 0
+    harris[-3:] = 0
+    harris[:, :3] = 0
+    harris[:, -3:] = 0
+
+    return harris
+
+
+def harris_corner_detector(image, min_distance=10, threshold=0.1, eps=1e-6):
+    """Return corners from a Harris response image
+
+    params
+    -------
+    harrisim: ndarray
+
+    min_distance: int, optional, default: 10
+        minimum number of pixels separating corners and image boundary
+
+    threshold: float, optional, default: 0.1
+
+    eps: float, optional, default: 1e-6
+
+    returns:
+    --------
+    array: coordinates
+    """
+    harrisim = _compute_harris_response(image, eps=eps)
+    corner_threshold = np.max(harrisim.ravel()) * threshold
+    # find top corner candidates above a threshold
+    # corner_threshold = max(harrisim.ravel()) * threshold
+    harrisim_t = (harrisim >= corner_threshold) * 1
+
+    # get coordinates of candidates
+    candidates = harrisim_t.nonzero()
+    coords = [(candidates[0][c], candidates[1][c]) for c
+               in range(len(candidates[0]))]
+
+    # ...and their values
+    candidate_values = [harrisim[c[0]][c[1]] for c in coords]
+
+    # sort candidates
+    index = np.argsort(candidate_values)
+
+    # store allowed point locations in array
+    allowed_locations = np.zeros(harrisim.shape)
+    allowed_locations[min_distance:-min_distance,
+                      min_distance:-min_distance] = 1
+
+    # select the best points taking min_distance into account
+    filtered_coords = []
+    for i in index:
+        if allowed_locations[coords[i][0]][coords[i][1]] == 1:
+            filtered_coords.append(coords[i])
+            allowed_locations[
+              (coords[i][0] - min_distance):(coords[i][0] + min_distance),
+              (coords[i][1] - min_distance):(coords[i][1] + min_distance)] = 0
+
+    return np.array(filtered_coords)

From 5d8110bab201b80191de8907ddfe78ff7dfb68dd Mon Sep 17 00:00:00 2001
From: Nelle Varoquaux <nelle.varoquaux@gmail.com>
Date: Thu, 22 Dec 2011 12:27:06 +0100
Subject: [PATCH 2/7] Added tests

---
 doc/examples/plot_harris.py         |  5 +++--
 skimage/filter/harris.py            |  3 ++-
 skimage/filter/tests/test_harris.py | 23 +++++++++++++++++++++++
 3 files changed, 28 insertions(+), 3 deletions(-)
 create mode 100644 skimage/filter/tests/test_harris.py

diff --git a/doc/examples/plot_harris.py b/doc/examples/plot_harris.py
index 258925ed..6cb02ba1 100644
--- a/doc/examples/plot_harris.py
+++ b/doc/examples/plot_harris.py
@@ -6,11 +6,12 @@ Harris Corner detector
 The Harris corner filter detects interest points using edge detection in many
 direction.
 """
+
 from matplotlib import pyplot as plt
 from matplotlib import cm
 
 from skimage import data
-from skimage.filter import harris
+from skimage.filter import harris_corner_detector
 
 
 def plot_harris_points(image, filtered_coords):
@@ -26,5 +27,5 @@ def plot_harris_points(image, filtered_coords):
 
 
 im = data.lena().astype(float)
-filtered_coords = harris.harris_corner_detector(im, 6)
+filtered_coords = harris_corner_detector(im, 6)
 plot_harris_points(im, filtered_coords)
diff --git a/skimage/filter/harris.py b/skimage/filter/harris.py
index 53b58b01..b5abe7a3 100644
--- a/skimage/filter/harris.py
+++ b/skimage/filter/harris.py
@@ -1,6 +1,7 @@
 #
 # Harris detector
 #
+# Inspired from Solem's implementation
 # http://www.janeriksolem.net/2009/01/harris-corner-detector-in-python.html
 
 import numpy as np
@@ -56,7 +57,7 @@ def harris_corner_detector(image, min_distance=10, threshold=0.1, eps=1e-6):
 
     params
     -------
-    harrisim: ndarray
+    harrisim: ndarray of floats
 
     min_distance: int, optional, default: 10
         minimum number of pixels separating corners and image boundary
diff --git a/skimage/filter/tests/test_harris.py b/skimage/filter/tests/test_harris.py
new file mode 100644
index 00000000..19d8cd3b
--- /dev/null
+++ b/skimage/filter/tests/test_harris.py
@@ -0,0 +1,23 @@
+import numpy as np
+
+import unittest
+
+from skimage.filter import harris_corner_detector
+
+
+class TestHarris(unittest.TestCase):
+
+    def test_square_image(self):
+        im = np.zeros((50, 50)).astype(float)
+        im[:25, :25] = 1.
+        results = harris_corner_detector(im)
+        self.assertTrue(results.any())
+        self.assertTrue(len(results) == 1)
+
+    def test_noisy_square_image(self):
+        im = np.zeros((50, 50)).astype(float)
+        im[:25, :25] = 1.
+        im = im + np.random.uniform(size=im.shape) * .5
+        results = harris_corner_detector(im)
+        assert results.any()
+        assert len(results) == 1

From 7ff98597e2cf1a92e4a1b957926a43fdfbb29e1e Mon Sep 17 00:00:00 2001
From: Nelle Varoquaux <nelle.varoquaux@gmail.com>
Date: Thu, 22 Dec 2011 22:08:27 +0100
Subject: [PATCH 3/7] Small fixes on the Harris corner detector -
 documentation, method renaming etc

---
 CONTRIBUTORS.txt                    |  1 +
 doc/examples/plot_harris.py         | 21 ++++++-------
 skimage/filter/__init__.py          |  2 +-
 skimage/filter/harris.py            | 48 +++++++++++++++++------------
 skimage/filter/tests/test_harris.py | 23 ++++++++------
 5 files changed, 54 insertions(+), 41 deletions(-)

diff --git a/CONTRIBUTORS.txt b/CONTRIBUTORS.txt
index 007f267a..2e97d7e1 100644
--- a/CONTRIBUTORS.txt
+++ b/CONTRIBUTORS.txt
@@ -83,6 +83,7 @@
 
 - Nelle Varoquaux
   Renaming of the package to ``skimage``.
+  Harris corner detector
 
 - W. Randolph Franklin
   Point in polygon test.
diff --git a/doc/examples/plot_harris.py b/doc/examples/plot_harris.py
index 6cb02ba1..9c499a3b 100644
--- a/doc/examples/plot_harris.py
+++ b/doc/examples/plot_harris.py
@@ -3,29 +3,28 @@
 Harris Corner detector
 ===============================================================================
 
-The Harris corner filter detects interest points using edge detection in many
-direction.
+The Harris corner filter detects interest points using edge detection in
+multiple direction.
 """
 
 from matplotlib import pyplot as plt
-from matplotlib import cm
 
-from skimage import data
-from skimage.filter import harris_corner_detector
+from skimage import data, img_as_float
+from skimage.filter import harris
 
 
 def plot_harris_points(image, filtered_coords):
     """ plots corners found in image"""
 
-    plt.subplot(111)
-    plt.imshow(image, cmap=cm.gray)
+    plt.plot()
+    plt.imshow(image)
     plt.plot([p[1] for p in filtered_coords],
-                [p[0] for p in filtered_coords],
-                'b.')
+             [p[0] for p in filtered_coords],
+             'b.')
     plt.axis('off')
     plt.show()
 
 
-im = data.lena().astype(float)
-filtered_coords = harris_corner_detector(im, 6)
+im = img_as_float(data.lena())
+filtered_coords = harris(im, 6)
 plot_harris_points(im, filtered_coords)
diff --git a/skimage/filter/__init__.py b/skimage/filter/__init__.py
index 6075291f..529331f0 100644
--- a/skimage/filter/__init__.py
+++ b/skimage/filter/__init__.py
@@ -5,4 +5,4 @@ from edges import sobel, hsobel, vsobel, hprewitt, vprewitt, prewitt
 from tv_denoise import tv_denoise
 from rank_order import rank_order
 from thresholding import threshold_otsu
-from harris import harris_corner_detector
+from harris import harris
diff --git a/skimage/filter/harris.py b/skimage/filter/harris.py
index b5abe7a3..303431dd 100644
--- a/skimage/filter/harris.py
+++ b/skimage/filter/harris.py
@@ -10,18 +10,20 @@ from scipy import ndimage
 
 def _compute_harris_response(image, eps=1e-6):
     """Compute the Harris corner detector response function
-        for each pixel in the image
+    for each pixel in the image
 
-    Params
-    -------
-    image: ndarray
+    Parameters
+    ----------
+    image: ndarray of floats
+        input image
 
-    eps: float, optional, default: 1e-6
-    normalisation factor
+    eps: float, optional
+        normalisation factor
 
     Returns
     --------
-    ndarray
+    features: (M, 2) ndarray
+        Harris image response
     """
     if len(image.shape) == 3:
         image = image.mean(axis=2)
@@ -42,8 +44,10 @@ def _compute_harris_response(image, eps=1e-6):
 
     # Non maximum filter of size 3
     harris_max = ndimage.maximum_filter(harris, 3, mode='constant')
-    harris *= harris == harris_max
-    # Remove the image corners
+    mask = (harris == harris_max)
+    harris *= mask
+
+    # Remove the image borders
     harris[:3] = 0
     harris[-3:] = 0
     harris[:, :3] = 0
@@ -52,23 +56,26 @@ def _compute_harris_response(image, eps=1e-6):
     return harris
 
 
-def harris_corner_detector(image, min_distance=10, threshold=0.1, eps=1e-6):
+def harris(image, min_distance=10, threshold=0.1, eps=1e-6):
     """Return corners from a Harris response image
 
-    params
-    -------
-    harrisim: ndarray of floats
+    Parameters
+    ----------
+    image: ndarray of floats
+        Input image
 
-    min_distance: int, optional, default: 10
-        minimum number of pixels separating corners and image boundary
+    min_distance: int, optional
+        minimum number of pixels separating interest points and image boundary
 
-    threshold: float, optional, default: 0.1
+    threshold: float, optional
+        relative threshold impacting the number of interest points.
 
-    eps: float, optional, default: 1e-6
+    eps: float, optional
+        Normalisation factor
 
     returns:
     --------
-    array: coordinates
+    array: coordinates of interest points
     """
     harrisim = _compute_harris_response(image, eps=eps)
     corner_threshold = np.max(harrisim.ravel()) * threshold
@@ -78,8 +85,9 @@ def harris_corner_detector(image, min_distance=10, threshold=0.1, eps=1e-6):
 
     # get coordinates of candidates
     candidates = harrisim_t.nonzero()
-    coords = [(candidates[0][c], candidates[1][c]) for c
-               in range(len(candidates[0]))]
+    coords = np.concatenate((candidates[0].reshape((len(candidates[0]), 1)),
+                             candidates[1].reshape((len(candidates[0]), 1))),
+                            axis=1)
 
     # ...and their values
     candidate_values = [harrisim[c[0]][c[1]] for c in coords]
diff --git a/skimage/filter/tests/test_harris.py b/skimage/filter/tests/test_harris.py
index 19d8cd3b..c78d3e26 100644
--- a/skimage/filter/tests/test_harris.py
+++ b/skimage/filter/tests/test_harris.py
@@ -1,23 +1,28 @@
 import numpy as np
 
-import unittest
-
-from skimage.filter import harris_corner_detector
+from skimage.filter import harris
+from skimage import img_as_float
 
 
-class TestHarris(unittest.TestCase):
-
+class TestHarris():
     def test_square_image(self):
         im = np.zeros((50, 50)).astype(float)
         im[:25, :25] = 1.
-        results = harris_corner_detector(im)
-        self.assertTrue(results.any())
-        self.assertTrue(len(results) == 1)
+        results = harris(im)
+        assert results.any()
+        assert len(results) == 1
 
     def test_noisy_square_image(self):
         im = np.zeros((50, 50)).astype(float)
         im[:25, :25] = 1.
         im = im + np.random.uniform(size=im.shape) * .5
-        results = harris_corner_detector(im)
+        results = harris(im)
         assert results.any()
         assert len(results) == 1
+
+    def test_squared_dot(self):
+        im = np.zeros((50, 50))
+        im[4:8, 4:8] = 1
+        im = img_as_float(im)
+        results = harris(im)
+        assert results == np.array([6, 6])

From bc0f77622e28e4c9eb27a2c0bb58f8261dd3c8e3 Mon Sep 17 00:00:00 2001
From: Nelle Varoquaux <nelle.varoquaux@gmail.com>
Date: Fri, 23 Dec 2011 17:21:55 +0100
Subject: [PATCH 4/7] Added tests for harris, and small fixes

---
 skimage/filter/harris.py            | 10 ++++------
 skimage/filter/tests/test_harris.py | 18 ++++++++++++++++--
 2 files changed, 20 insertions(+), 8 deletions(-)

diff --git a/skimage/filter/harris.py b/skimage/filter/harris.py
index 303431dd..2d48f7ec 100644
--- a/skimage/filter/harris.py
+++ b/skimage/filter/harris.py
@@ -22,7 +22,7 @@ def _compute_harris_response(image, eps=1e-6):
 
     Returns
     --------
-    features: (M, 2) ndarray
+    image: (M, N) ndarray
         Harris image response
     """
     if len(image.shape) == 3:
@@ -85,12 +85,10 @@ def harris(image, min_distance=10, threshold=0.1, eps=1e-6):
 
     # get coordinates of candidates
     candidates = harrisim_t.nonzero()
-    coords = np.concatenate((candidates[0].reshape((len(candidates[0]), 1)),
-                             candidates[1].reshape((len(candidates[0]), 1))),
-                            axis=1)
+    coords = np.transpose(candidates)
 
     # ...and their values
-    candidate_values = [harrisim[c[0]][c[1]] for c in coords]
+    candidate_values = harrisim[candidates]
 
     # sort candidates
     index = np.argsort(candidate_values)
@@ -103,7 +101,7 @@ def harris(image, min_distance=10, threshold=0.1, eps=1e-6):
     # select the best points taking min_distance into account
     filtered_coords = []
     for i in index:
-        if allowed_locations[coords[i][0]][coords[i][1]] == 1:
+        if allowed_locations[tuple(coords[i])] == 1:
             filtered_coords.append(coords[i])
             allowed_locations[
               (coords[i][0] - min_distance):(coords[i][0] + min_distance),
diff --git a/skimage/filter/tests/test_harris.py b/skimage/filter/tests/test_harris.py
index c78d3e26..853a00a0 100644
--- a/skimage/filter/tests/test_harris.py
+++ b/skimage/filter/tests/test_harris.py
@@ -1,8 +1,10 @@
 import numpy as np
 
-from skimage.filter import harris
+from skimage import data
 from skimage import img_as_float
 
+from skimage.filter import harris
+
 
 class TestHarris():
     def test_square_image(self):
@@ -24,5 +26,17 @@ class TestHarris():
         im = np.zeros((50, 50))
         im[4:8, 4:8] = 1
         im = img_as_float(im)
+        results = harris(im, min_distance=3)
+        print results
+        assert (results == np.array([[6, 6]])).all()
+
+    def test_rotated_lena(self):
+        """
+        The harris filter should yield the same results with an image and it's
+        rotation.
+        """
+        im = img_as_float(data.lena().mean(axis=2))
         results = harris(im)
-        assert results == np.array([6, 6])
+        im_rotated = im.T
+        results_rotated = harris(im_rotated)
+        assert (results[:, 0] == results_rotated[:, 1]).all()

From 374c44671517952d3cf0f97c588fddcac05db70a Mon Sep 17 00:00:00 2001
From: Nelle Varoquaux <nelle.varoquaux@gmail.com>
Date: Fri, 23 Dec 2011 17:28:57 +0100
Subject: [PATCH 5/7] Added parameter to Harris corner detector to set the
 deviation for the gaussian kernel of the harris response computation

---
 skimage/filter/harris.py | 16 ++++++++++++----
 1 file changed, 12 insertions(+), 4 deletions(-)

diff --git a/skimage/filter/harris.py b/skimage/filter/harris.py
index 2d48f7ec..8e8a46ec 100644
--- a/skimage/filter/harris.py
+++ b/skimage/filter/harris.py
@@ -8,7 +8,7 @@ import numpy as np
 from scipy import ndimage
 
 
-def _compute_harris_response(image, eps=1e-6):
+def _compute_harris_response(image, eps=1e-6, gaussian_deviation=1):
     """Compute the Harris corner detector response function
     for each pixel in the image
 
@@ -20,6 +20,9 @@ def _compute_harris_response(image, eps=1e-6):
     eps: float, optional
         normalisation factor
 
+    gaussian_deviation: integer, optional
+        standard deviation used for the Gaussian kernel
+
     Returns
     --------
     image: (M, N) ndarray
@@ -29,7 +32,7 @@ def _compute_harris_response(image, eps=1e-6):
         image = image.mean(axis=2)
 
     # derivatives
-    image = ndimage.gaussian_filter(image, 1)
+    image = ndimage.gaussian_filter(image, gaussian_deviation)
     imx = ndimage.sobel(image, axis=0, mode='constant')
     imy = ndimage.sobel(image, axis=1, mode='constant')
 
@@ -56,7 +59,8 @@ def _compute_harris_response(image, eps=1e-6):
     return harris
 
 
-def harris(image, min_distance=10, threshold=0.1, eps=1e-6):
+def harris(image, min_distance=10, threshold=0.1, eps=1e-6,
+           gaussian_deviation=1):
     """Return corners from a Harris response image
 
     Parameters
@@ -73,11 +77,15 @@ def harris(image, min_distance=10, threshold=0.1, eps=1e-6):
     eps: float, optional
         Normalisation factor
 
+    gaussian_deviation: integer, optional
+        standard deviation used for the Gaussian kernel
+
     returns:
     --------
     array: coordinates of interest points
     """
-    harrisim = _compute_harris_response(image, eps=eps)
+    harrisim = _compute_harris_response(image, eps=eps,
+                    gaussian_deviation=gaussian_deviation)
     corner_threshold = np.max(harrisim.ravel()) * threshold
     # find top corner candidates above a threshold
     # corner_threshold = max(harrisim.ravel()) * threshold

From 3c2988b4bc3a7ad83f730c8c78ae5b0e7dd02541 Mon Sep 17 00:00:00 2001
From: Nelle Varoquaux <nelle.varoquaux@gmail.com>
Date: Sun, 25 Dec 2011 09:54:47 +0100
Subject: [PATCH 6/7] small fixes on the Harris' documentation: added capital
 letters where needed

---
 skimage/filter/harris.py | 16 ++++++++--------
 1 file changed, 8 insertions(+), 8 deletions(-)

diff --git a/skimage/filter/harris.py b/skimage/filter/harris.py
index 8e8a46ec..7eeecd30 100644
--- a/skimage/filter/harris.py
+++ b/skimage/filter/harris.py
@@ -15,13 +15,13 @@ def _compute_harris_response(image, eps=1e-6, gaussian_deviation=1):
     Parameters
     ----------
     image: ndarray of floats
-        input image
+        Input image
 
     eps: float, optional
-        normalisation factor
+        Normalisation factor
 
     gaussian_deviation: integer, optional
-        standard deviation used for the Gaussian kernel
+        Standard deviation used for the Gaussian kernel
 
     Returns
     --------
@@ -69,16 +69,16 @@ def harris(image, min_distance=10, threshold=0.1, eps=1e-6,
         Input image
 
     min_distance: int, optional
-        minimum number of pixels separating interest points and image boundary
+        Minimum number of pixels separating interest points and image boundary
 
     threshold: float, optional
-        relative threshold impacting the number of interest points.
+        Relative threshold impacting the number of interest points.
 
     eps: float, optional
         Normalisation factor
 
     gaussian_deviation: integer, optional
-        standard deviation used for the Gaussian kernel
+        Standard deviation used for the Gaussian kernel
 
     returns:
     --------
@@ -86,9 +86,9 @@ def harris(image, min_distance=10, threshold=0.1, eps=1e-6,
     """
     harrisim = _compute_harris_response(image, eps=eps,
                     gaussian_deviation=gaussian_deviation)
-    corner_threshold = np.max(harrisim.ravel()) * threshold
+
     # find top corner candidates above a threshold
-    # corner_threshold = max(harrisim.ravel()) * threshold
+    corner_threshold = np.max(harrisim.ravel()) * threshold
     harrisim_t = (harrisim >= corner_threshold) * 1
 
     # get coordinates of candidates

From 7285cbe29604a5a0d362348ad823d04578039a2a Mon Sep 17 00:00:00 2001
From: Nelle Varoquaux <nelle.varoquaux@gmail.com>
Date: Thu, 5 Jan 2012 18:20:39 +0100
Subject: [PATCH 7/7] =?UTF-8?q?Added=20Ga=C3=ABl=20Varoquaux=20to=20the=20?=
 =?UTF-8?q?corner=20detector=20contributors?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 CONTRIBUTORS.txt | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/CONTRIBUTORS.txt b/CONTRIBUTORS.txt
index 2e97d7e1..a5f83b5c 100644
--- a/CONTRIBUTORS.txt
+++ b/CONTRIBUTORS.txt
@@ -87,3 +87,6 @@
 
 - W. Randolph Franklin
   Point in polygon test.
+
+- Gaël Varoquaux
+  Harris corner detector