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minor doc changes

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Vighnesh Birodkar
2014-03-15 01:10:58 +05:30
parent 3cf6bffe84
commit d5f9ebfc8f
2 changed files with 4 additions and 5 deletions
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skimage/feature/_hessian_det_appx.pyx
+2 -2
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@@ -22,7 +22,7 @@ cdef inline int _clip(np.int_t x, np.int_t low, np.int_t high):
Returns
-------
x : int
`x` clipped between 'high' and `low`.
`x` clipped between `high` and `low`.
"""
@@ -77,7 +77,7 @@ def _hessian_det_appx(np.ndarray[np.int_t, ndim=2] image, float sigma):
"""Computes the approximate Hessian Determinant over an image.
This method uses box filters over integral images to compute the
approximate Hessian Determinant as described in [1].
approximate Hessian Determinant as described in [1]_.
Parameters
----------
skimage/feature/blob.py
+2 -3
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@@ -309,13 +309,12 @@ def blob_doh(image, min_sigma=1, max_sigma=30, num_sigma=10, threshold=500,
Blobs are found using the Determinant of Hessian method [1]_. For each blob
found, the method returns its coordinates and the standard deviation
of the Gaussian Kernel used for the Hessian matrix whose determinant
detected the blob. Determinant of Hessians is approximated using [2]_
detected the blob. Determinant of Hessians is approximated using [2]_.
Parameters
----------
image : ndarray
Input grayscale image, blobs are assumed to be light on dark
background (white on black).
Input grayscale image.Blobs can either be light on dark or vice versa.
min_sigma : float, optional
The minimum standard deviation for Gaussian Kernel used to compute
Hessian matrix. Keep this low to detect smaller blobs.