ORB: An efficient alternative to SIFT or SURF

  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},
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… 

Robust image matching via ORB feature and VFC for mismatch removal

A robust image match approach based on ORB feature and VFC for mismatch removal, which has the same performance as SIFT with lower cost and is efficient and robust.

Robust Features Matching Using Scale-invariant Center Surround Filter

This paper presents a method for extracting distinctive invariant features from images, coined SCFD (Scale-invariant Center surround Filter Detection), and demonstrates through experiments how it can be used to perform reliable and high-precision matching between different views of an object or scene, yet can be computed much faster.

Robust Recognition against Illumination Variations Based on SIFT

This paper presents an approach to make a more robust algorithm against real world illumination changes and variations in direction of the light source on the authors' object of interest, by using a set of training images for sampling these variations from their SIFT keypoints.

Better than SIFT?

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

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.

2D Image Features Detector And Descriptor Selection Expert System

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

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.

A Novel Binary Feature Descriptor for Accelerated Robust Matching

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

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

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.



SURF: Speeded Up Robust Features

In this paper, we present a novel scale- and rotation-invariant interest point detector and descriptor, coined SURF (Speeded Up Robust Features). It approximates or even outperforms previously

Distinctive Image Features from Scale-Invariant Keypoints

The Scale-Invariant Feature Transform (or SIFT) algorithm is a highly robust method to extract and consequently match distinctive invariant features from images. These features can then be used to

PCA-SIFT: a more distinctive representation for local image descriptors

  • Yan KeR. Sukthankar
  • Computer Science
    Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004.
  • 2004
This paper examines (and improves upon) the local image descriptor used by SIFT, and demonstrates that the PCA-based local descriptors are more distinctive, more robust to image deformations, and more compact than the standard SIFT representation.

BRIEF: Binary Robust Independent Elementary Features

We propose to use binary strings as an efficient feature point descriptor, which we call BRIEF. We show that it is highly discriminative even when using relatively few bits and can be computed using

Multi-image matching using multi-scale oriented patches

This paper describes a novel multi-view matching framework based on a new type of invariant feature that is used in an automatic 2D panorama stitcher that has been extensively tested on hundreds of sample inputs.

Faster and Better: A Machine Learning Approach to Corner Detection

A new heuristic for feature detection is presented and, using machine learning, a feature detector is derived from this which can fully process live PAL video using less than 5 percent of the available processing time.

Keypoint Signatures for Fast Learning and Recognition

This paper proposes a descriptor that can be learned online fast enough to handle virtually unlimited numbers of key points, and relies on the fact that if the authors train a Randomized Tree classifier to recognize a number of keypoints extracted from an image database, all other keypoints can be characterized in terms of their response to these classification trees.

Machine Learning for High-Speed Corner Detection

It is shown that machine learning can be used to derive a feature detector which can fully process live PAL video using less than 7% of the available processing time.

A Combined Corner and Edge Detector

The problem we are addressing in Alvey Project MMI149 is that of using computer vision to understand the unconstrained 3D world, in which the viewed scenes will in general contain too wide a

SURF: Speeded-Up Robust Features

In this document, the SURF detector-descriptor scheme used in the OpenSURF library is discussed in detail. First the algorithm is analysed from a theoretical standpoint to provide a detailed overview