Let's write a code that demonstrates the ORB feature detection and binary descriptor algorithm. An oriented FAST detection method and the rotated BRIEF descriptors are used by this algorithm. As compared to BRIEF, ORB is more scale and rotation invariant, but even this applies the Hamming distance metric for matching, which is more efficient. Hence, this method is preferred over BRIEF when considering real-time applications:

from skimage import transform as transform
from skimage.feature import (match_descriptors, ORB, plot_matches)
img1 = rgb2gray(imread('../images/me5.jpg'))
img2 = transform.rotate(img1, 180)
affine_trans = transform.AffineTransform(scale=(1.3, 1.1), rotation=0.5, translation=(0, -200))
img3 = transform.warp(img1, affine_trans)
img4 = transform.resize(rgb2gray(imread('../images/me6.jpg')), img1.shape, anti_aliasing=True)
descriptor_extractor = ORB(n_keypoints=200)
descriptor_extractor.detect_and_extract(img1)
keypoints1, descriptors1 = descriptor_extractor.keypoints, descriptor_extractor.descriptors
descriptor_extractor.detect_and_extract(img2)
keypoints2, descriptors2 = descriptor_extractor.keypoints, descriptor_extractor.descriptors
descriptor_extractor.detect_and_extract(img3)
keypoints3, descriptors3 = descriptor_extractor.keypoints, descriptor_extractor.descriptors
descriptor_extractor.detect_and_extract(img4)
keypoints4, descriptors4 = descriptor_extractor.keypoints, descriptor_extractor.descriptors
matches12 = match_descriptors(descriptors1, descriptors2, cross_check=True)
matches13 = match_descriptors(descriptors1, descriptors3, cross_check=True)
matches14 = match_descriptors(descriptors1, descriptors4, cross_check=True)
fig, axes = pylab.subplots(nrows=3, ncols=1, figsize=(20,25))
pylab.gray()
plot_matches(axes[0], img1, img2, keypoints1, keypoints2, matches12)
axes[0].axis('off'), axes[0].set_title("Original Image vs. Transformed Image", size=20)
plot_matches(axes[1], img1, img3, keypoints1, keypoints3, matches13)
axes[1].axis('off'), axes[1].set_title("Original Image vs. Transformed Image", size=20)
plot_matches(axes[2], img1, img4, keypoints1, keypoints4, matches14)
axes[2].axis('off'), axes[2].set_title("Image1 vs. Image2", size=20)
pylab.show()

You can see the output and how we matched it with an ORB detector and binary descriptor:

The next screenshots show the output images of the code block and the ORB keypoints for the images to be matched, along with the matches shown with lines. First the algorithm tries to match an image with its affine transformed version, and then two different images with a same object.

Orientation 1:

Orientation 2:

Orientation 3: