BRIEF binary descriptor

This example demonstrates the BRIEF binary description algorithm. The descriptor consists of relatively few bits and can be computed using a set of intensity difference tests. The short binary descriptor results in low memory footprint and very efficient matching based on the Hamming distance metric. BRIEF does not provide rotation-invariance. Scale-invariance can be achieved by detecting and extracting features at different scales.

Original Image vs. Transformed Image, Original Image vs. Transformed Image
from skimage import data
from skimage import transform
from skimage.feature import (match_descriptors, corner_peaks, corner_harris,
                             plot_matches, BRIEF)
from skimage.color import rgb2gray
import matplotlib.pyplot as plt


img1 = rgb2gray(data.astronaut())
tform = transform.AffineTransform(scale=(1.2, 1.2), translation=(0, -100))
img2 = transform.warp(img1, tform)
img3 = transform.rotate(img1, 25)

keypoints1 = corner_peaks(corner_harris(img1), min_distance=5,
                          threshold_rel=0.1)
keypoints2 = corner_peaks(corner_harris(img2), min_distance=5,
                          threshold_rel=0.1)
keypoints3 = corner_peaks(corner_harris(img3), min_distance=5,
                          threshold_rel=0.1)

extractor = BRIEF()

extractor.extract(img1, keypoints1)
keypoints1 = keypoints1[extractor.mask]
descriptors1 = extractor.descriptors

extractor.extract(img2, keypoints2)
keypoints2 = keypoints2[extractor.mask]
descriptors2 = extractor.descriptors

extractor.extract(img3, keypoints3)
keypoints3 = keypoints3[extractor.mask]
descriptors3 = extractor.descriptors

matches12 = match_descriptors(descriptors1, descriptors2, cross_check=True)
matches13 = match_descriptors(descriptors1, descriptors3, cross_check=True)

fig, ax = plt.subplots(nrows=2, ncols=1)

plt.gray()

plot_matches(ax[0], img1, img2, keypoints1, keypoints2, matches12)
ax[0].axis('off')
ax[0].set_title("Original Image vs. Transformed Image")

plot_matches(ax[1], img1, img3, keypoints1, keypoints3, matches13)
ax[1].axis('off')
ax[1].set_title("Original Image vs. Transformed Image")


plt.show()

Total running time of the script: ( 0 minutes 0.657 seconds)

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