Merge pull request #3956 from alexandrejaguar/docs_faridderiv

Improving documentation and tests for directional filters
scikit-image · Jun 26, 2019 · 7cd1218 · 7cd1218
2 parents 44ab69e + b0f2afe
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diff --git a/doc/examples/edges/plot_edge_filter.py b/doc/examples/edges/plot_edge_filter.py
@@ -90,21 +90,22 @@
 plt.show()
 
 ######################################################################
-# As in the example above, we illustrate the rotational invariance
-# of the filters in this example. The top row shows a
-# rotationally invariant image along with the angle of its analytical gradient.
-# The other two rows contain the difference between the different gradient
-# approximations (Sobel, Prewitt, Scharr & Farid) and analytical gradient.
+# As in the previous example, here we illustrate the rotational invariance of
+# the filters. The top row shows a rotationally invariant image along with the
+# angle of its analytical gradient. The other two rows contain the difference
+# between the different gradient approximations (Sobel, Prewitt, Scharr &
+# Farid) and analytical gradient.
 #
 # The Farid & Simoncelli derivative filters [4]_, [5]_  are the most
 # rotationally invariant, but require a 5x5 kernel, which is computationally
 # more intensive than a 3x3 kernel.
 #
-# .. [4] Farid, Hany, and Eero P. Simoncelli. "Differentiation of discrete
-#     multidimensional signals."
-#     IEEE Transactions on image processing 13.4 (2004): 496-508.
+# .. [4] Farid, H. and Simoncelli, E. P., "Differentiation of discrete
+#        multidimensional signals", IEEE Transactions on Image Processing 13(4):
+#        496-508, 2004. :DOI:`10.1109/TIP.2004.823819`
 #
-# .. [5] https://en.wikipedia.org/wiki/Image_derivatives
+# .. [5] Wikipedia, "Farid and Simoncelli Derivatives." Available at:
+#        <https://en.wikipedia.org/wiki/Image_derivatives#Farid_and_Simoncelli_Derivatives>
 
 
 x, y = np.mgrid[-10:10:255j, -10:10:255j]

diff --git a/skimage/filters/edges.py b/skimage/filters/edges.py
@@ -548,7 +548,7 @@ def laplace(image, ksize=3, mask=None):
 
 
 def farid(image, mask=None):
-    """Find the edge magnitude using the Farid & Simoncelli transform.
+    """Find the edge magnitude using the Farid transform.
 
     Parameters
     ----------
@@ -562,7 +562,7 @@ def farid(image, mask=None):
     Returns
     -------
     output : 2-D array
-        The Farid & Simoncelli edge map.
+        The Farid edge map.
 
     See also
     --------
@@ -577,13 +577,11 @@ def farid(image, mask=None):
 
     References
     ----------
-    .. [1] Farid, Hany, and Eero P. Simoncelli. "Differentiation of discrete
-    multidimensional signals."
-        IEEE Transactions on image processing 13.4 (2004): 496-508.
-
-
-    .. [2] https://en.wikipedia.org/wiki/Image_derivatives
-    #Farid_and_Simoncelli_Derivatives
+    .. [1] Farid, H. and Simoncelli, E. P., "Differentiation of discrete
+           multidimensional signals", IEEE Transactions on Image Processing
+           13(4): 496-508, 2004. :DOI:`10.1109/TIP.2004.823819`
+    .. [2] Wikipedia, "Farid and Simoncelli Derivatives." Available at:
+           <https://en.wikipedia.org/wiki/Image_derivatives#Farid_and_Simoncelli_Derivatives>
 
     Examples
     --------
@@ -599,8 +597,7 @@ def farid(image, mask=None):
 
 
 def farid_h(image, mask=None):
-    """Find the horizontal edges of an image using the Farid & Simoncelli
-    transform.
+    """Find the horizontal edges of an image using the Farid transform.
 
     Parameters
     ----------
@@ -614,23 +611,20 @@ def farid_h(image, mask=None):
     Returns
     -------
     output : 2-D array
-        The Farid & Simoncelli edge map.
+        The Farid edge map.
 
     Notes
     -----
-    The kernel was constructed using the 5-tap weights from [1]
-
+    The kernel was constructed using the 5-tap weights from [1].
 
     References
     ----------
-    .. [1] Farid, Hany, and Eero P. Simoncelli. "Differentiation of discrete
-    multidimensional signals."
-        IEEE Transactions on image processing 13.4 (2004): 496-508.
-    .. [2] Farid, Hany, and Eero P. Simoncelli. "Optimally
-    rotation-equivariant directional derivative kernels."
-        International Conference on Computer Analysis of Images and Patterns.
-        Springer, Berlin, Heidelberg, 1997.
-
+    .. [1] Farid, H. and Simoncelli, E. P., "Differentiation of discrete
+           multidimensional signals", IEEE Transactions on Image Processing
+           13(4): 496-508, 2004. :DOI:`10.1109/TIP.2004.823819`
+    .. [2] Farid, H. and Simoncelli, E. P. "Optimally rotation-equivariant
+           directional derivative kernels", In: 7th International Conference on
+           Computer Analysis of Images and Patterns, Kiel, Germany. Sep, 1997.
     """
     assert_nD(image, 2)
     image = img_as_float(image)
@@ -639,8 +633,7 @@ def farid_h(image, mask=None):
 
 
 def farid_v(image, mask=None):
-    """Find the vertical edges of an image using the Farid & Simoncelli
-    transform.
+    """Find the vertical edges of an image using the Farid transform.
 
     Parameters
     ----------
@@ -654,19 +647,17 @@ def farid_v(image, mask=None):
     Returns
     -------
     output : 2-D array
-        The Farid & Simoncelli edge map.
+        The Farid edge map.
 
     Notes
     -----
-    The kernel was constructed using the 5-tap weights from [1]
-
+    The kernel was constructed using the 5-tap weights from [1].
 
     References
     ----------
-    .. [1] Farid, Hany, and Eero P. Simoncelli. "Differentiation of discrete
-    multidimensional signals."
-        IEEE Transactions on image processing 13.4 (2004): 496-508.
-
+    .. [1] Farid, H. and Simoncelli, E. P., "Differentiation of discrete
+           multidimensional signals", IEEE Transactions on Image Processing
+           13(4): 496-508, 2004. :DOI:`10.1109/TIP.2004.823819`
     """
     assert_nD(image, 2)
     image = img_as_float(image)