From 6f561130e6600bebe37f9fec22195155f6496c7d Mon Sep 17 00:00:00 2001 From: emmanuelle Date: Sun, 30 Nov 2014 17:08:49 +0100 Subject: [PATCH] Added references on rotation invariance and filters. --- doc/examples/plot_edge_filter.py | 16 +++++++++++----- 1 file changed, 11 insertions(+), 5 deletions(-) diff --git a/doc/examples/plot_edge_filter.py b/doc/examples/plot_edge_filter.py index d3e169a8..0bb6c567 100644 --- a/doc/examples/plot_edge_filter.py +++ b/doc/examples/plot_edge_filter.py @@ -36,11 +36,17 @@ plt.tight_layout() Different operators compute different finite-difference approximations of the gradient. For example, the Scharr filter results in a better rotational -variance than other filters such as the Sobel filter. The difference between -the two filters is illustrated below on an image that is the discretization of -a rotation-invariant continuous function. The discrepancy between the two -filters is stronger for regions of the image where the direction of the -gradient is close to diagonals, and for regions with high spatial frequencies. +variance than other filters such as the Sobel filter [1]_ [2]_. The difference +between the two filters is illustrated below on an image that is the +discretization of a rotation-invariant continuous function. The discrepancy +between the two filters is stronger for regions of the image where the +direction of the gradient is close to diagonal, and for regions with high +spatial frequencies. + +.. [1] http://en.wikipedia.org/wiki/Sobel_operator#Alternative_operators + +.. [2] B. Jaehne, H. Scharr, and S. Koerkel. Principles of filter design. In + Handbook of Computer Vision and Applications. Academic Press, 1999. """ x, y = np.ogrid[:100, :100]