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Added method reference to example.

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Vighnesh Birodkar
2014-04-28 02:23:48 +05:30
parent 580f5d2dfd
commit cc76e8a1da
1 changed files with 6 additions and 4 deletions
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doc/examples/plot_blob.py
+6 -4
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@@ -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.
"""