15 October 2012 Stable detection of expanded target by the use of boosting random ferns
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Proceedings Volume 8420, 6th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Optical System Technologies for Manufacturing and Testing; 84200A (2012) https://doi.org/10.1117/12.977284
Event: 6th International Symposium on Advanced Optical Manufacturing and Testing Technologies (AOMATT 2012), 2012, Xiamen, China
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.
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Li Deng, Chunhong Wang, Changhui Rao, "Stable detection of expanded target by the use of boosting random ferns", 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; https://doi.org/10.1117/12.977284
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