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题名:
Stable detection of expanded target by the use of boosting random ferns
作者: Deng, Li1,2,3; Wang, Chunhong1,2; Rao, Changhui1,2
出版日期: 2012
会议名称: Proceedings of SPIE: 6th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Optical System Technologies for Manufacturing and Testing
会议日期: 2012
DOI: 10.1117/12.977284
通讯作者: Deng, L.
中文摘要: 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. © 2012 SPIE.
英文摘要: 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. © 2012 SPIE.
收录类别: Ei
语种: 英语
卷号: 8420
ISSN号: 0277786X
文章类型: 会议论文
页码: 84200A
Citation statistics:
内容类型: 会议论文
URI标识: http://ir.ioe.ac.cn/handle/181551/7782
Appears in Collections:自适应光学技术研究室(八室)_会议论文

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作者单位: 1. Laboratory on Adaptive Optics, Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu, Sichuan 610209, China
2. Key Laboratory on Adaptive Optics, Chinese Academy of Sciences, Chengdu, Sichuan 610209, China
3. Graduate University, Chinese Academy of Sciences, Beijing 100039, China

Recommended Citation:
Deng, Li,Wang, Chunhong,Rao, Changhui. Stable detection of expanded target by the use of boosting random ferns[C]. 见:Proceedings of SPIE: 6th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Optical System Technologies for Manufacturing and Testing. 2012.
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