[1] He Lianghua, Zou Cairong, Zhao Li, et al. Optimal shape space and searching in the active shape model [J]. Journal of Southeast University (English Edition), 2005, 21 (3): 263-267. [doi:10.3969/j.issn.1003-7985.2005.03.004]

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Optimal shape space and searching in the active shape model()

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

Volumn:

21

Issue:

2005 3

Page:

263-267

Research Field:

Computer Science and Engineering

Publishing date:

2005-09-30

Info

Title:

Optimal shape space and searching in the active shape model

Author(s):

He Lianghua¹;  2;  Zou Cairong²;  Zhao Li²;  Hu Die²

¹ Research Center of Learning Science, Southeast University, Nanjing 210096, China
² Department of Radio Engineering, Southeast University, Nanjing 210096, China

Keywords:

active shape model;  shape subspace;  search subspace;  principal component analysis

PACS:

TP391

DOI:

10.3969/j.issn.1003-7985.2005.03.004

Abstract:

A novel idea, called the optimal shape subspace(OSS)is first proposed for optimizing active shape model(ASM)search.It is constructed from the principal shape subspace and the principal shape variance subspace.It allows the reconstructed shape to vary more than that reconstructed in the standard ASM shape space, hence it is more expressive in representing shapes in real life.Then a cost function is developed, based on a study on the search process.An optimal searching method using the feedback information provided by the evaluation cost is proposed to improve the performance of ASM alignment.Experimental results show that the proposed OSS can offer the maximum shape variation with reserving the principal information and a unique local optimal shape is acquired after optimal searching.The combination of OSS and optimal searching can improve the ASM performance greatly.

References:

[1] Cootes T F, Taylor C J, Cooper D H, et al.Active shape models:their training and application [J].Computer Vision and Image Understanding, 1995, 61(1):38-59.
[2] Rogers M, Graham J.Robust active shape model search [A].In:Proceedings of the European Conference on Computer Vision [C].Copenhagen, Denmark, 2002.517-530.
[3] Huang Xiangsheng, Li S Z, Wang Yangsheng.Evaluation of face alignment solutions using statistical learning [A].In:Proceedings of Sixth IEEE International Conference on Automatic Face and Gesture Recognition [C].Seoul, South Korea, 2004.213-218.
[4] Zhao Ming, Chen Chun, Li S Z, et al.Subspace analysis and optimization for AAM based face alignment [A].In: Proceedings of Sixth IEEE International Conference on Automatic Face and Gesture Recognition [C].Seoul, South Korea, 2004.290-295.
[5] Martinez A, Benavente R.The AR face database [R].Barcelona, Spain: Computer Vision Center(CVC), 1998.
[6] Phillips P J, Moon H, Rizvi S A, et al.The FERET evaluation methodology for face-recognition algorithms [J].IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(10):1090-1104.
[7] Li S Z, Zhang Zhenqiu.FloatBoost learning and statistical face detection [J].IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004, 26(9):1112-1123.

Memo

Memo:

Biographies: He Lianghua(1977—), male, graduate;Zou Cairong(corresponding author), male, doctor, professor, cairong@seu.edu.cn.

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