Open AccessLightweight generic random ferns for multi‐target augmented reality on mobile devices
Author(s) -
Lee Suwon,
Yang Hyun S.
Publication year - 2013
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
ISSN - 1350-911X
DOI - 10.1049/el.2013.0754
Subject(s) - computer science , augmented reality , mobile device , artificial intelligence , classifier (uml) , computer vision , object (grammar) , matching (statistics) , cognitive neuroscience of visual object recognition , representation (politics) , binary number , pattern recognition (psychology) , random forest , mathematics , statistics , arithmetic , politics , political science , law , operating system
Proposed use lightweight generic random ferns (LGRF), a fast keypoint classifier designed for multi‐target augmented reality (AR) on mobile devices. LGRF uses binary features of image patches for both object recognition and keypoint matching of multiple objects, and stores probabilities in a single bit representation to reduce memory requirements. As a result, LGRF can perform simultaneous object recognition and keypoint matching in real time with low memory consumption, making it suitable for multi‐target AR on mobile devices.