An experimental evaluation of binary feature descriptors

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Scopus citations

Abstract

Efficient and compact representation of local image patches in the form of features descriptors that are distinctive/robust as well as fast to compute and match is an essential and inevitable step for many computer vision applications. One category of these representations is the binary descriptors which have been shown to be successful alternatives providing similar performance to their floating-point counterparts while being efficient to compute and store. In this paper, a comprehensive performance evaluation of the current state-of-the-art binary descriptors; namely, BRIEF, ORB, BRISK, FREAK, and LATCH is presented in the context of image matching. This performance evaluation highlights several points regarding the performance characteristics of binary descriptors under various geometric and photometric transformations of images.

Original languageEnglish
Title of host publicationProceedings of the International Conference on Advanced Intelligent Systems and Informatics 2017
EditorsMohamed F. Tolba, Tarek Gaber, Khaled Shaalan, Aboul Ella Hassanien
PublisherSpringer Verlag
Pages181-191
Number of pages11
ISBN (Print)9783319648606
DOIs
StatePublished - 2018
Externally publishedYes
Event3rd International Conference on Advanced Intelligent Systems and Informatics, AISI 2017 - Cairo, Egypt
Duration: 9 Sep 201711 Sep 2017

Publication series

NameAdvances in Intelligent Systems and Computing
Volume639
ISSN (Print)2194-5357

Conference

Conference3rd International Conference on Advanced Intelligent Systems and Informatics, AISI 2017
Country/TerritoryEgypt
CityCairo
Period9/09/1711/09/17

Fingerprint

Dive into the research topics of 'An experimental evaluation of binary feature descriptors'. Together they form a unique fingerprint.

Cite this