ORB: An efficient alternative to SIFT or SURF

@article{Rublee2011ORBAE,
  title={ORB: An efficient alternative to SIFT or SURF},
  author={Ethan Rublee and Vincent Rabaud and Kurt Konolige and Gary R. Bradski},
  journal={2011 International Conference on Computer Vision},
  year={2011},
  pages={2564-2571}
}
Feature matching is at the base of many computer vision problems, such as object recognition or structure from motion. Current methods rely on costly descriptors for detection and matching. In this paper, we propose a very fast binary descriptor based on BRIEF, called ORB, which is rotation invariant and resistant to noise. We demonstrate through experiments how ORB is at two orders of magnitude faster than SIFT, while performing as well in many situations. The efficiency is tested on several… 

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TLDR
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TLDR
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TLDR
This study compares the performance of several state-of-the art image descriptors including several recent binary descriptors and finds that SIFT is still the most accurate performer in both application settings.

An Improved ORB Feature Point Matching Algorithm

TLDR
The experimental results show that the improved ORB feature point matching algorithm borrowing the idea of Binary Robust Invariant Scalable Keypoints (BRISK) algorithm to uniformly sample the extracted feature points has good scale invariance and is suitable for applications requiring high real-time and large scale changes.

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TLDR
This paper proposes a solution for a particular case study on object recognition of industrial parts based on hierarchical classification by reducing the number of instances, and demonstrates that this method performs better than using just one method like ORB, SIFT or FREAK, despite being fairly slower.

Object recognition with ORB and its Implementation on FPGA

TLDR
An overview of a general methods of object recognition and significance of ORB over SIFT and SURF in different cases is given and an idea to implement ORB algorithm on FPGA to increase the execution speed by utilizing the reconfigurable nature and pipelining of theFPGA is provided.

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TLDR
A novel feature descriptor with binary strings that is very fast to compute and match with good performance of being invariant to scale, rotation and noise to address the current requirement of stereo matching between images.

Feature Descriptors for Tracking by Detection: a Benchmark

TLDR
An extensive evaluation of the performance of local descriptors for tracking applications shows that binary descriptors like ORB or BRISK have comparable results to SIFT or AKAZE due to a higher number of key-points.

B-SIFT: A Simple and Effective SIFT for Real-Time Application

TLDR
A binary descriptor which can be computed using a simple intensity difference of relatively few bits is introduced, and an efficient and effective algorithm named B-SIFT is proposed which obtains 1–2 orders of magnitude speed up while preserving competitive discriminant ability.
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