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题名:
Shape Matching under Affine Transformation Using Normalization and Multi-scale Area Integral Features
作者: Cai HY(蔡慧英); Zhu F(朱枫); Hao YM(郝颖明); Lu RR(鲁荣荣)
作者部门: 光电信息技术研究室
通讯作者: 蔡慧英
会议名称: International Symposium on Infrared Technology and Application and the International Symposiums on Robot Sensing and Advanced Control
会议日期: May 9-11, 2016
会议地点: Beijing
会议录: Proceedings of SPIE - The International Society for Optical Engineering
会议录出版者: SPIE
会议录出版地: Bellingham, WA
出版日期: 2016
页码: 1-7
收录类别: EI ; CPCI(ISTP)
ISSN号: 0277-786X
ISBN号: 978-1-5106-0772-9
关键词: Shape matching ; Affine transformation ; shape normalization ; Multi-scale area integral features
摘要: Shape Matching under Affine Transformation (SMAT) is an important issue in shape analysis. Most of the existing SMAT methods are sensitive to noise or complicated because they usually need to extract the edge points or compute the high order function of the shape. To solve these problems, a new SMAT method which combines the low order shape normalization and the multi-scale area integral features is proposed. First, the shapes with affine transformation are normalized into their orthogonal representations according to the moments and an equivalent resample. This procedure transforms the shape by several linear operations: translations, scaling, and rotation, following by a resample operation. Second, the Multi-Scale Area Integral Features (MSAIF) of the shapes which are invariant to the orthogonal transformation (rotation and reflection transformation) are extracted. The MSAIF is a signature achieved through concatenating the area integral feature at a range of scales from fine to coarse. The area integral feature is an integration of the feature values, which are computed by convoluting the shape with an isotropic kernel and taking the complement, over the shape domain following by the normalization using the area of the shape. Finally, the matching of different shapes is performed according to the dissimilarity which is measured with the optimal transport distance. The performance of the proposed method is tested on the car dataset and the multi-view curve dataset. Experimental results show that the proposed method is efficient and robust, and can be used in many shape analysis works.
语种: 英语
产权排序: 1
EI收录号: 20170503310112
WOS记录号: WOS:000391228600067
Citation statistics:
内容类型: 会议论文
URI标识: http://ir.sia.cn/handle/173321/19169
Appears in Collections:光电信息技术研究室_会议论文

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Recommended Citation:
Cai HY,Zhu F,Hao YM,et al. Shape Matching under Affine Transformation Using Normalization and Multi-scale Area Integral Features[C]. International Symposium on Infrared Technology and Application and the International Symposiums on Robot Sensing and Advanced Control. Beijing. May 9-11, 2016.Shape Matching under Affine Transformation Using Normalization and Multi-scale Area Integral Features.
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