Proceedings ArticleDOI
ORB: An efficient alternative to SIFT or SURF
Ethan Rublee,Vincent Rabaud,Kurt Konolige,Gary Bradski +3 more
- pp 2564-2571
TLDR
This paper proposes a very fast binary descriptor based on BRIEF, called ORB, which is rotation invariant and resistant to noise, and demonstrates through experiments how ORB is at two orders of magnitude faster than SIFT, while performing as well in many situations.Abstract:
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 real-world applications, including object detection and patch-tracking on a smart phone.read more
Citations
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Journal ArticleDOI
ORB-SLAM: A Versatile and Accurate Monocular SLAM System
TL;DR: ORB-SLAM as discussed by the authors is a feature-based monocular SLAM system that operates in real time, in small and large indoor and outdoor environments, with a survival of the fittest strategy that selects the points and keyframes of the reconstruction.
Journal ArticleDOI
scikit-image: Image processing in Python
Stefan van der Walt,Johannes L. Schonberger,Juan Nunez-Iglesias,François Boulogne,Joshua D. Warner,Neil Yager,Emmanuelle Gouillart,Tony S. Yu +7 more
TL;DR: The advantages of open source to achieve the goals of the scikit-image library are highlighted, and several real-world image processing applications that use scik it-image are showcased.
Journal ArticleDOI
ORB-SLAM: a Versatile and Accurate Monocular SLAM System
TL;DR: A survival of the fittest strategy that selects the points and keyframes of the reconstruction leads to excellent robustness and generates a compact and trackable map that only grows if the scene content changes, allowing lifelong operation.
Journal ArticleDOI
ORB-SLAM2: An Open-Source SLAM System for Monocular, Stereo, and RGB-D Cameras
Raul Mur-Artal,Juan D. Tardós +1 more
TL;DR: ORB-SLAM2, a complete simultaneous localization and mapping (SLAM) system for monocular, stereo and RGB-D cameras, including map reuse, loop closing, and relocalization capabilities, is presented, being in most cases the most accurate SLAM solution.
Proceedings ArticleDOI
Deformable Convolutional Networks
TL;DR: Deformable convolutional networks as discussed by the authors augment the spatial sampling locations in the modules with additional offsets and learn the offsets from the target tasks, without additional supervision, which can readily replace their plain counterparts in existing CNNs and can be easily trained end-to-end by standard backpropagation.
References
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Distinctive Image Features from Scale-Invariant Keypoints
TL;DR: This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene and can robustly identify objects among clutter and occlusion while achieving near real-time performance.
Distinctive Image Features from Scale-Invariant Keypoints
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Proceedings ArticleDOI
A Combined Corner and Edge Detector
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Book ChapterDOI
SURF: speeded up robust features
TL;DR: A novel scale- and rotation-invariant interest point detector and descriptor, coined SURF (Speeded Up Robust Features), which approximates or even outperforms previously proposed schemes with respect to repeatability, distinctiveness, and robustness, yet can be computed and compared much faster.
Proceedings ArticleDOI
Video Google: a text retrieval approach to object matching in videos
TL;DR: An approach to object and scene retrieval which searches for and localizes all the occurrences of a user outlined object in a video, represented by a set of viewpoint invariant region descriptors so that recognition can proceed successfully despite changes in viewpoint, illumination and partial occlusion.