ORB: An efficient alternative to SIFT or SURF

Ethan Rublee; Vincent Rabaud; Kurt Konolige; Gary Bradski · OpenAlex · 2011

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Ethan Rublee; Vincent Rabaud; Kurt Konolige; Gary Bradski

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OpenAlex

Year

2011

Language

English

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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.

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APA 7

Rublee, E, Rabaud, V, Konolige, K, & Bradski, G. (2011). ORB: An efficient alternative to SIFT or SURF. https://doi.org/10.1109/iccv.2011.6126544

MLA

Rublee, Ethan, et al. "ORB: An efficient alternative to SIFT or SURF." 2011. https://doi.org/10.1109/iccv.2011.6126544.

Chicago

Rublee, Ethan, Vincent Rabaud, Kurt Konolige, and Gary Bradski. 2011. "ORB: An efficient alternative to SIFT or SURF.". https://doi.org/10.1109/iccv.2011.6126544.

Harvard

Rublee, E. et al. 2011, ORB: An efficient alternative to SIFT or SURF, OpenAlex, available at: https://doi.org/10.1109/iccv.2011.6126544 [Accessed 7 Aug. 2026].

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Title: ORB: An efficient alternative to SIFT or SURF

Author / contributors: Ethan Rublee; Vincent Rabaud; Kurt Konolige; Gary Bradski

Publisher: OpenAlex

Publication year: 2011

Language: English

Subjects

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Orb (optics); Scale-invariant feature transform; Computer science; Artificial intelligence; Computer vision; Object detection; Cognitive neuroscience of visual object recognition; Matching (statistics); Feature extraction; Rotation (mathematics); Feature (linguistics); Invariant (physics)