wassname/scikit-image
1
0
Fork 0
mirror of https://github.com/wassname/scikit-image.git synced 2026-07-25 13:30:51 +08:00
Code Issues Packages Projects Releases Wiki Activity

Improve BRIEF doc string

Browse Source
This commit is contained in:
Johannes Schönberger
2014-01-22 18:56:43 -05:00
parent 72f2963ca7
commit 279d892789
1 changed files with 12 additions and 12 deletions
Download Patch File Download Diff File Expand all files Collapse all files
skimage/feature/brief.py
+12 -12
View File
@@ -12,15 +12,15 @@ class BRIEF(DescriptorExtractor):
"""BRIEF binary descriptor extractor.
BRIEF (Binary Robust Independent Elementary Features) is an efficient
feature point descriptor. It it is highly discriminative even when using
relatively few bits and can be computed using simple intensity difference
feature point descriptor. It is highly discriminative even when using
relatively few bits and is computed using simple intensity difference
tests.
For each keypoint intensity comparisons are carried out for a specifically
For each keypoint, intensity comparisons are carried out for a specifically
distributed number N of pixel-pairs resulting in a binary descriptor of
length N. The descriptor similarity can thus be computed using the Hamming
distance which leads to very good matching performance in contrast to the
L2 norm.
length N. For binary descriptors the Hamming distance can be used for
feature matching, which leads to lower computational cost in comparison to
the L2 norm.
Parameters
----------
@@ -35,12 +35,12 @@ class BRIEF(DescriptorExtractor):
around keypoints.
sample_seed : int, optional
Seed for the random sampling of the decision pixel-pairs. From a square
window with length patch_size, pixel pairs are sampled using the `mode`
parameter to build the descriptors using intensity comparison. The
value of `sample_seed` must be the same for the images to be matched
while building the descriptors.
window with length `patch_size`, pixel pairs are sampled using the
`mode` parameter to build the descriptors using intensity comparison.
The value of `sample_seed` must be the same for the images to be
matched while building the descriptors.
sigma : float, optional
Standard deviation of the Gaussian low pass filter applied to the image
Standard deviation of the Gaussian low-pass filter applied to the image
to alleviate noise sensitivity, which is strongly recommended to obtain
discriminative and good descriptors.
@@ -139,7 +139,7 @@ class BRIEF(DescriptorExtractor):
image = _prepare_grayscale_input_2D(image)
# Gaussian Low pass filtering to alleviate noise sensitivity
# Gaussian low-pass filtering to alleviate noise sensitivity
image = np.ascontiguousarray(gaussian_filter(image, self.sigma))
# Sampling pairs of decision pixels in patch_size x patch_size window