¿Qué es el detector ORB?

What is ORB detector?

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.

ORB is an efficient alternative to SIFT or SURF algorithms used for feature extraction, in computation cost, matching performance, and mainly the patents. SIFT and SURF are patented and you are supposed to pay them for its use.

So orb algorithm uses a multiscale image pyramid. Once orb has created a pyramid it uses the fast algorithm to detect keypoints in the image. By detecting keypoints at each level orb is effectively locating key points at a different scale. In this way, ORB is partial scale invariant.

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.

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.

ABSTRACT. Feature detection is a major operation in various computer vision systems. The KAZE algorithm and its improved version, Accelerated- KAZE (AKAZE), are considered as the first algorithms to detect fea- tures by building a scale space using nonlinear diffusion.

Keypoints are calculated using various different algorithms, ORB(Oriented FAST and Rotated BRIEF) technique uses the FAST algorithm to calculate the keypoints. FAST stands for Features from Accelerated Segments Test. FAST calculates keypoints by considering pixel brightness around a given area.

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.

But ORB is not !!! 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.

But if we consider about ORB it gets same no.of keypoints both original and rotated images and matches 100%. Also, I notice that ORB execute fast than others. 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. Nowadays SURF not in use.

Yes, SIFT and SURF are patented and you are supposed to pay them for its use. But ORB is not !!! 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.

ORB’s main contributions are as follows: The addition of a fast and accurate orientation component to FAST The efficient computation of oriented BRIEF features Analysis of variance and correlation of oriented BRIEF features