diff --git a/skimage/detection/_template.pyx b/skimage/detection/_template.pyx
index 67a462d7..90bfe666 100644
--- a/skimage/detection/_template.pyx
+++ b/skimage/detection/_template.pyx
@@ -163,7 +163,8 @@ def match_template(np.ndarray[float, ndim=2, mode="c"] image,
         np.ndarray[float, ndim=2, mode="c"] template, int num_type):
     # convolve the image with template by frequency domain multiplication
     cdef np.ndarray[np.double_t, ndim=2] result
-    result = np.ascontiguousarray(fftconvolve(image, np.fliplr(template), mode="valid"), dtype=np.double)
+    result = np.ascontiguousarray(fftconvolve(image, np.fliplr(template),
+                                              mode="valid"), dtype=np.double)
     # calculate squared integral images used for normalization
     cdef np.ndarray[np.double_t, ndim=2,  mode="c"] integral_sum
     cdef np.ndarray[np.double_t, ndim=2,  mode="c"] integral_sqr
@@ -193,12 +194,16 @@ def match_template(np.ndarray[float, ndim=2, mode="c"] image,
             num = result[i, j]
             window_mean2 = 0
             if num_type == 1:
-                t = sum_integral(integral_sum, i, j, i + template.shape[0], j + template.shape[1])
+                t = sum_integral(integral_sum, i, j,
+                                 i + template.shape[0],
+                                 j + template.shape[1])
                 window_mean2 = t * t * inv_area
                 num -= t*template_mean
 
             # calculate squared template window sum in the image
-            window_sum2 = sum_integral(integral_sqr, i, j, i + template.shape[0], j + template.shape[1])
+            window_sum2 = sum_integral(integral_sqr, i, j,
+                                       i + template.shape[0],
+                                       j + template.shape[1])
             normed = sqrt(window_sum2 - window_mean2) * template_norm
             # enforce some limits
             if fabs(num) < normed:
diff --git a/skimage/detection/template.py b/skimage/detection/template.py
index eb780799..3d8b9a0c 100644
--- a/skimage/detection/template.py
+++ b/skimage/detection/template.py
@@ -20,10 +20,13 @@ def match_template_cv(image, template, out=None,  method="norm-coeff"):
     Returns
     -------
     output : ndarray, dtype=float
-        Correlation results between 0.0 and 1.0, maximum indicating the most probable match.
+        Correlation results between 0.0 and 1.0, maximum indicating the most
+        probable match.
     """
     if out == None:
-        out = np.empty((image.shape[0] - template.shape[0] + 1,image.shape[1] - template.shape[1] + 1), dtype=image.dtype)
+        out = np.empty((image.shape[0] - template.shape[0] + 1,
+                        image.shape[1] - template.shape[1] + 1),
+                       dtype=image.dtype)
     if method == "norm-corr":
         cv.MatchTemplate(image, template, out, cv.CV_TM_CCORR_NORMED)
     elif method == "norm-corr":
diff --git a/skimage/detection/tests/test_template.py b/skimage/detection/tests/test_template.py
index 987a3496..b5cc9bcd 100644
--- a/skimage/detection/tests/test_template.py
+++ b/skimage/detection/tests/test_template.py
@@ -23,7 +23,8 @@ def test_template():
             if not found_positions:
                 found_positions.append((x, y))
             for position in found_positions:
-                distance = np.sqrt((x - position[0]) ** 2 + (y - position[1]) ** 2)
+                distance = np.sqrt((x - position[0]) ** 2 +
+                                   (y - position[1]) ** 2)
                 if distance > delta:
                     found_positions.append((x, y))
             result[y, x] = 0
@@ -34,7 +35,8 @@ def test_template():
             print x, y
             found = False
             for position in found_positions:
-                distance = np.sqrt((x - position[0]) ** 2 + (y - position[1]) ** 2)
+                distance = np.sqrt((x - position[0]) ** 2 +
+                                   (y - position[1]) ** 2)
                 if distance < delta:
                     found = True
             assert found