2D ndarray of binary descriptors of size :None:None:`descriptor_size` for Q keypoints after filtering out border keypoints with value at an index (i, j)
either being True
or False
representing the outcome of the intensity comparison for i-th keypoint on j-th decision pixel-pair. It is Q == np.sum(mask)
.
Mask indicating whether a keypoint has been filtered out ( False
) or is described in the descriptors
array ( True
).
BRIEF (Binary Robust Independent Elementary Features) is an efficient 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 distributed number N of pixel-pairs resulting in a binary descriptor of 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.
Size of BRIEF descriptor for each keypoint. Sizes 128, 256 and 512 recommended by the authors. Default is 256.
Length of the two dimensional square patch sampling region around the keypoints. Default is 49.
Probability distribution for sampling location of decision pixel-pairs around keypoints.
Seed for the random sampling of the decision pixel-pairs. From a square window with length :None:None:`patch_size`, pixel pairs are sampled using the :None:None:`mode` parameter to build the descriptors using intensity comparison. The value of :None:None:`sample_seed` must be the same for the images to be matched while building the descriptors.
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.
BRIEF binary descriptor extractor.
>>> from skimage.feature import (corner_harris, corner_peaks, BRIEF,This example is valid syntax, but we were not able to check execution
... match_descriptors)
... import numpy as np
... square1 = np.zeros((8, 8), dtype=np.int32)
... square1[2:6, 2:6] = 1
... square1 array([[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]], dtype=int32)
>>> square2 = np.zeros((9, 9), dtype=np.int32)This example is valid syntax, but we were not able to check execution
... square2[2:7, 2:7] = 1
... square2 array([[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=int32)
>>> keypoints1 = corner_peaks(corner_harris(square1), min_distance=1,This example is valid syntax, but we were not able to check execution
... threshold_rel=0)
... keypoints2 = corner_peaks(corner_harris(square2), min_distance=1,
... threshold_rel=0)
... extractor = BRIEF(patch_size=5)
... extractor.extract(square1, keypoints1)
... descriptors1 = extractor.descriptors
... extractor.extract(square2, keypoints2)
... descriptors2 = extractor.descriptors
... matches = match_descriptors(descriptors1, descriptors2)
... matches array([[0, 0], [1, 1], [2, 2], [3, 3]])
>>> keypoints1[matches[:, 0]] array([[2, 2], [2, 5], [5, 2], [5, 5]])This example is valid syntax, but we were not able to check execution
>>> keypoints2[matches[:, 1]] array([[2, 2], [2, 6], [6, 2], [6, 6]])See :
The following pages refer to to this document either explicitly or contain code examples using this.
skimage.feature.brief.BRIEF
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