Lightweight generic random ferns for multi‐target augmented reality on mobile devices. Issue 13 (1st June 2013)

• Record Type: Journal Article
• Authors: Lee, Suwon; Yang, Hyun S.
• Is Part Of: Electronics letters. Volume 49:Issue 13(2013)
• Page Start: 800
• Page End: 802
• Publication Date: 2013-06-01
• Subjects: augmented reality -- image matching -- mobile handsets -- real‐time systems; lightweight generic random ferns -- multitarget augmented reality -- mobile devices -- LGRF -- fast keypoint classifier -- binary features -- image patches -- object recognition -- multitarget AR -- real time -- memory requirements reduction -- single bit representation -- multiple objects -- keypoint matching; augmented reality -- image matching -- mobile handsets -- real‐time systems; lightweight generic random ferns -- multitarget augmented reality -- mobile devices -- LGRF -- fast keypoint classifier -- binary features -- image patches -- object recognition -- multitarget AR -- real time -- memory requirements reduction -- single bit representation -- multiple objects -- keypoint matching; Electronics -- Periodicals; 621.381
• DOI: 10.1049/el.2013.0754
• Languages: English
• ISSNs: 0013-5194

Abstract

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.