Title :
Lightweight generic random ferns for multi-target augmented reality on mobile devices
Author :
Suwon Lee ; Yang, Hyung Suk
Author_Institution :
Dept. of Comput. Sci., KAIST, Daejeon, South Korea
Abstract :
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.
Keywords :
augmented reality; image matching; mobile handsets; real-time systems; LGRF; binary features; fast keypoint classifier; image patches; keypoint matching; lightweight generic random ferns; memory requirements reduction; mobile devices; multiple objects; multitarget AR; multitarget augmented reality; object recognition; real time; single bit representation;
Journal_Title :
Electronics Letters
DOI :
10.1049/el.2013.0754