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题名:
Contour level object detection with top-down information
作者: Yu, Huapeng1,2,3; Chang, Yongxin1,2,3; Lu, Pei1,2,3; Xu, Zhiyong1; Fu, Chengyu1; Wang, Yafei2
刊名: Optik
出版日期: 2014
卷号: 125, 期号:11, 页码:2708-2712
学科分类: Data processing - Semantics
DOI: 10.1016/j.ijleo.2013.11.029
通讯作者: Yu, H. (yuhuapeng@uestc.edu.cn)
文章类型: 期刊论文
中文摘要: This paper presents a contour level object detection approach. In contrast to conventional bounding box results, we give out the salient closed contour of the object, which provides a possibility of semantic analysis for the object. We get the salient closed contour with Ratio Contour algorithm. The top-down information needed by salient closed contour extraction is based on the well-known Bag-of-Features methodology. Our top-down information based contour extraction and completion is much more efficient and robust than many related approaches lack of the top-down information. We also propose a novel post-processing framework for object detection. With low threshold and a refined binary classifier, we can get stable high performance. We evaluate our approaches on UIUC cars dataset. We show that our approaches apparently improve the performance of object detections under clutter. © 2014 Elsevier GmbH. All rights reserved.
英文摘要: This paper presents a contour level object detection approach. In contrast to conventional bounding box results, we give out the salient closed contour of the object, which provides a possibility of semantic analysis for the object. We get the salient closed contour with Ratio Contour algorithm. The top-down information needed by salient closed contour extraction is based on the well-known Bag-of-Features methodology. Our top-down information based contour extraction and completion is much more efficient and robust than many related approaches lack of the top-down information. We also propose a novel post-processing framework for object detection. With low threshold and a refined binary classifier, we can get stable high performance. We evaluate our approaches on UIUC cars dataset. We show that our approaches apparently improve the performance of object detections under clutter. © 2014 Elsevier GmbH. All rights reserved.
收录类别: SCI ; Ei
项目资助者: Graduate Innovation Fund of Chinese Academy of Sciences
语种: 英语
WOS记录号: WOS:000337118300049
ISSN号: 00304026
Citation statistics:
内容类型: 期刊论文
URI标识: http://ir.ioe.ac.cn/handle/181551/5077
Appears in Collections:光电探测与信号处理研究室(五室)_期刊论文

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作者单位: 1. Institute of Optics and Electronics, Chinese Academy of Sciences, P.O. Box 350, Shuangliu, Chengdu 610209, Sichuan Province, China
2. School of Optoelectronic Information, University of Electronic Science and Technology of China, Chengdu 610054, China
3. Graduate University of Chinese Academy of Sciences, Beijing 100039, China

Recommended Citation:
Yu, Huapeng,Chang, Yongxin,Lu, Pei,et al. Contour level object detection with top-down information[J]. Optik,2014,125(11):2708-2712.
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