What is the advantage of ORB feature extractor over SIFT and SURF?

After comparing SIFT, SURF and ORB, we can notice ORB is the fast algorithm. From the result, we can assume ORB gets keypoint more efficient than others.

Which is better ORB or SIFT?

We showed that ORB is the fastest algorithm while SIFT performs the best in the most scenarios. For special case when the angle of rotation is proportional to 90 degrees, ORB and SURF outperforms SIFT and in the noisy images, ORB and SIFT show almost similar performances.

How does an ORB work?

ORB uses BRIEF descriptors but as the BRIEF performs poorly with rotation. So what ORB does is to rotate the BRIEF according to the orientation of keypoints. Using the orientation of the patch, its rotation matrix is found and rotates the BRIEF to get the rotated version.

What is ORB feature?

Oriented FAST and rotated BRIEF (ORB) is a fast robust local feature detector, first presented by Ethan Rublee et al. in 2011, that can be used in computer vision tasks like object recognition or 3D reconstruction.

Which is better SIFT or SURF?

SIFT and SURF are most useful approaches to detect and matching of features because of it is invariant to scale, rotate, translation, illumination, and blur. SIFT is better than SURF in different scale images. SURF is 3 times faster than SIFT because using of integral image and box filter.

Why is SURF faster than SIFT?

It is partly inspired by the scale-invariant feature transform (SIFT) descriptor. The standard version of SURF is several times faster than SIFT and claimed by its authors to be more robust against different image transformations than SIFT.

What is ORB in Python?

ORB is basically a fusion of FAST keypoint detector and BRIEF descriptor with many modifications to enhance the performance. First it use FAST to find keypoints, then apply Harris corner measure to find top N points among them. It also use pyramid to produce multiscale-features.

What is brief descriptor?

BRIEF is a general-purpose feature point descriptor that can be combined with arbitrary detectors. It is robust to typical classes of photometric and geometric image transformations.