Share Email Print
cover

Proceedings Paper

Stable detection of expanded target by the use of boosting random ferns
Author(s): Li Deng; Chunhong Wang; Changhui Rao
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

This paper studies the problem of keypoints recognition of extended target which lacks of texture information, and introduces an approach of stable detection of these targets called boosting random ferns (BRF). As common descriptors in this circumstance do not work as well as usual cases, matching of keypoints is hence turned into classification task so as to make use of the trainable characteristic of classifier. The kernel of BRF is consisted of random ferns as the classifier and AdaBoost (Adaptive Boosting) as the frame so that accuracy of random ferns classifier can be boosted to a relatively high level. Experiments compare BRF with widely used SURF descriptor and single random ferns classifier. The result shows that BRF obtains higher recognition rate of keypoints. Besides, for image sequence, BRF provides stronger stability than SURF in target detection, which proves the efficiency of BRF aiming to extended target which lacks of texture information.

Paper Details

Date Published: 15 October 2012
PDF: 7 pages
Proc. SPIE 8420, 6th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Optical System Technologies for Manufacturing and Testing, 84200A (15 October 2012); doi: 10.1117/12.977284
Show Author Affiliations
Li Deng, Institute of Optics and Electronics (China)
Graduate Univ. of Chinese Academy of Sciences (China)
Chunhong Wang, Institute of Optics and Electronics (China)
Changhui Rao, Institute of Optics and Electronics (China)


Published in SPIE Proceedings Vol. 8420:
6th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Optical System Technologies for Manufacturing and Testing
Xiangdi Lin; Yoshiharu Namba; Tingwen Xing, Editor(s)

© SPIE. Terms of Use
Back to Top