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A novel multi-view object recognition in complex background
Chang, Yongxin1,2,3; Yu, Huapeng1,2,3; Xu, Zhiyong1; Fu, Chengyu1; Gao, Chunming2
Volume9255
Pages92553K
2015
Language英语
ISSN0277-786X
DOI10.1117/12.2065292
Indexed ByEi
Subtype会议论文
AbstractRecognizing objects from arbitrary aspects is always a highly challenging problem in computer vision, and most existing algorithms mainly focus on a specific viewpoint research. Hence, in this paper we present a novel recognizing framework based on hierarchical representation, part-based method and learning in order to recognize objects from different viewpoints. The learning evaluates the model"™s mistakes and feeds it back the detector to avid the same mistakes in the future. The principal idea is to extract intrinsic viewpoint invariant features from the unseen poses of object, and then to take advantage of these shared appearance features to support recognition combining with the improved multiple view model. Compared with other recognition models, the proposed approach can efficiently tackle multi-view problem and promote the recognition versatility of our system. For an quantitative valuation The novel algorithm has been tested on several benchmark datasets such as Caltech 101 and PASCAL VOC 2010. The experimental results validate that our approach can recognize objects more precisely and the performance outperforms others single view recognition methods. © 2015 SPIE.; Recognizing objects from arbitrary aspects is always a highly challenging problem in computer vision, and most existing algorithms mainly focus on a specific viewpoint research. Hence, in this paper we present a novel recognizing framework based on hierarchical representation, part-based method and learning in order to recognize objects from different viewpoints. The learning evaluates the model"™s mistakes and feeds it back the detector to avid the same mistakes in the future. The principal idea is to extract intrinsic viewpoint invariant features from the unseen poses of object, and then to take advantage of these shared appearance features to support recognition combining with the improved multiple view model. Compared with other recognition models, the proposed approach can efficiently tackle multi-view problem and promote the recognition versatility of our system. For an quantitative valuation The novel algorithm has been tested on several benchmark datasets such as Caltech 101 and PASCAL VOC 2010. The experimental results validate that our approach can recognize objects more precisely and the performance outperforms others single view recognition methods. © 2015 SPIE.
Conference NameProceedings of SPIE: 20th International Symposium on High Power Systems and Applications 2014, HPLS and A 2014
Conference Date2015
Citation statistics
Document Type会议论文
Identifierhttp://ir.ioe.ac.cn/handle/181551/7439
Collection光电工程总体研究室(一室)
Corresponding AuthorChang, Yongxin
Affiliation1. Institute of Optics and Electronics, Key Laboratory of Beam Control, Chinese Academy of Sciences, Chengdu, China
2. School of Optoelectronic Information, University of Electronic Science and Technology of China, Chengdu, China
3. University of Chinese Academy of Sciences, Beijing, China
Recommended Citation
GB/T 7714
Chang, Yongxin,Yu, Huapeng,Xu, Zhiyong,et al. A novel multi-view object recognition in complex background[C],2015:92553K.
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