OpenCV Feature Note(V) ORB

Paper:

http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.370.4395&rep=rep1&type=pdf

ORB

ORB algorithm full name is “Oriented FAST and Rotated BRIEF”

which means

1. ORB use detector like FAST to find potential corners, the document said “basically a fusion of FAST”
2. ORB use descriptors like BRIEf to describe keypoints, the document said “rBRIEF”
3. FAST algorithm isn’t “rotate invariant”, ORB add orientation to it
4. BRIEF descriptor isn’t as good as original after rotate, ORB use greedy search to solve it

Q1: How to assign FAST algorithm an orientation?

use “intensity centroid” point and “center” point as the vector direction

use image moments(图像矩) calculate the “intensity centroid”, refer to 03-contours-feature

In order to get rotate invariant

ORB must rotate BRIEF to domain orientation(called steer in the document)

but BRIEF will lose accurate after rotating.

Q2: BRIEF is rotation sensitivity, why?

BRIEF has a large variance and a mean near 0.5

if it rotated to domain orientation, the points in the image left & right corner.

that relative to domain orientation will look similar, then the variance will decrease

Q3: How to promote the rotated(steered) BRIEF result?

in paper use leaning method base on PASCAL 2006 set data

1. Run each test against all training patches.
2. Order the tests by their distance from a mean of 0.5, forming the vector T.
3. Greedy search

then the document said

"a greedy search among all possible binary tests to find the ones that have both high variance and means close to 0.5"

get the final result rBRIEF

Q4: How about the scale-invariant of ORB?

from the paper it talks a little about this

"FAST does not produce multi-scale features. We employ a scale pyramid of the image, and produce FAST features(filtered by Harris) at each level in the pyramid."

but no detail of this chapter

IN CONCLUSION:

ORB algorithm add orientation to FAST, use Harris filtered in pyramids and solve the rotation sensitivity of BRIEF

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