From cc76e8a1daa175a3c95d9c557ebe3c8a7f6891e7 Mon Sep 17 00:00:00 2001 From: Vighnesh Birodkar Date: Mon, 28 Apr 2014 02:23:48 +0530 Subject: [PATCH] Added method reference to example. --- doc/examples/plot_blob.py | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/doc/examples/plot_blob.py b/doc/examples/plot_blob.py index 358b57c4..33cc44c2 100644 --- a/doc/examples/plot_blob.py +++ b/doc/examples/plot_blob.py @@ -14,15 +14,17 @@ This is the most accurate and slowest approach. It computes the Laplacian of Gaussian images with successively increasing standard deviation and stacks them up in a cube. Blobs are local maximas in this cube. Detecting larger blobs is especially slower because of larger kernel sizes during -convolution. Only bright blobs on dark backgrounds are detected. +convolution. Only bright blobs on dark backgrounds are detected. See +:py:meth:`skimage.feature.blob_log` for usage. -Difference of Gaussian (LoG) +Difference of Gaussian (DoG) ---------------------------- This is a faster approximation of LoG approach. In this case the image is blurred with increasing standard deviations and the difference between two successively blurred images are stacked up in a cube. This method suffers from the same disadvantage as LoG approach for detecting larger -blobs. Blobs are again assumed to be bright on dark. +blobs. Blobs are again assumed to be bright on dark. See +:py:meth:`skimage.feature.blob_dog` for usage. Determinant of Hessian (DoH) ---------------------------- @@ -31,7 +33,7 @@ matrix of the Determinant of Hessian of the image. The detection speed is independent of the size of blobs as internally the implementation uses box filters instead of convolutions. Bright on dark as well as dark on bright blobs are detected. The downside is that small blobs (<3px) are not -detected accurately. +detected accurately. See :py:meth:`skimage.feature.blob_doh` for usage. """