How does orb work

It is possible to use L2 or L1 distance for matching uchar descriptors but results will be incorrect and findHomography returns unsatisfactory results. How are we doing? Please help us improve Stack Overflow. Take our short survey. Stack Overflow for Teams — Collaborate and share knowledge with a private group. Create a free Team What is Teams? Collectives on Stack Overflow. Learn more. Ask Question. Asked 10 years, 2 months ago. Active 9 years, 4 months ago. As long as the keypoint orientation is consistent across views, the correct set of points will be used to compute its descriptor.

BRIEF has an important property that each bit feature has a large variance and a mean near 0. But once it is oriented along keypoint direction, it loses this property and become more distributed.

High variance makes a feature more discriminative, since it responds differentially to inputs. Another desirable property is to have the tests uncorrelated, since then each test will contribute to the result. To resolve all these, ORB runs a greedy search among all possible binary tests to find the ones that have both high variance and means close to 0.

Problems are easier to locate when you use OpenCV directly. However, in case of Android, the added difficulty of NDK should not be forgotten. If available OpenCV functionality is necesary for the specific application, and no custom pixel level processing code is needed, JavaCV is the way to go. However, if a considerable amount of custom image processing code is required, Java code will slow you down, and you will need to switch to NDK, anyway.

In the latter case, OpenCV is the alternative to choose. Asked 4 Months ago Answers: 5 Viewed 26 times. The choice of 8 and 32 bits pattern is due to storage and efficiency issues. Storage issues Well, computers don't store individual bits. Efficiency issues Hamming distance is computed via an XOR operation. Ramy Al Zuhouri. Below is the implementation. Skip to content.

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