[1] Xing Weiwei, Liu Weibin, Yuan Baozong,. Part-level 3-D object classification with improved interpretation tree [J]. Journal of Southeast University (English Edition), 2007, 23 (2): 221-225. [doi:10.3969/j.issn.1003-7985.2007.02.014]

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Part-level 3-D object classification with improved interpretation tree()

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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

23

Issue:

2007 2

Page:

221-225

Research Field:

Computer Science and Engineering

Publishing date:

2007-06-30

Info

Title:

Part-level 3-D object classification with improved interpretation tree

Author(s):

Xing Weiwei¹;  Liu Weibin²;  Yuan Baozong²

¹ School of Software, Beijing Jiaotong University, Beijing 100044, China
² Institute of Information Science, Beijing Jiaotong University, Beijing 100044, China

Keywords:

3-D object classification;  shape match;  similarity measure;  interpretation tree

PACS:

TP391

DOI:

10.3969/j.issn.1003-7985.2007.02.014

Abstract:

For classifying unknown 3-D objects into a set of predetermined object classes, a part-level object classification method based on the improved interpretation tree is presented.The part-level representation is implemented, which enables a more compact shape description of 3-D objects.The proposed classification method consists of two key processing stages: the improved constrained search on an interpretation tree and the following shape similarity measure computation.By the classification method, both whole match and partial match with shape similarity ranks are achieved;especially, focus match can be accomplished, where different key parts may be labeled and all the matched models containing corresponding key parts may be obtained.A series of experiments show the effectiveness of the presented 3-D object classification method.

References:

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[3] Krivic J, Solina F.Part-level object recognition using superquadrics [J].Computer Vision and Image Understanding, 2004, 95(1):105-126.
[4] Huber D, Kapuria A, Donamukkala R R, et al.Parts-based 3-D object classification [C]//Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.Washington DC, 2004, 2:82-89.
[5] Borges D L, Fisher R B.Class-based recognition of 3-D objects represented by volumetric primitives [J].Image and Vision Computing, 1997, 15(8):655-664.
[6] Biederman I.Recognition-by-components:a theory of human image understanding [J].Psychological Review, 1987, 94:115-147.
[7] Grimson W E L, Huttenlocher D P.On the verification of hypothesized matches in model-based recognition [J].IEEE Trans on Pat Anal Machin Intel, 1991, 13(12):1201-1213.
[8] Xing Weiwei, Liu Weibin, Yuan Baozong.Superquadric similarity measure with spherical harmonics in 3-D object recognition [J].Chinese Journal of Electronics, 2005, 14(3):529-534.

Memo

Memo:

Biographies: Xing Weiwei(1980—), female, doctor, wwxing@bjtu.edu.cn;Yuan Baozong(1932—), male, doctor, professor, bzyuan@bjtu.edu.cn.

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