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Advances in Intelligent Systems pp 287–293Cite as

Kernel PCA and Nonlinear ASM

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Part of the book series: Advances in Intelligent and Soft Computing ((AINSC,volume 138))

Abstract

As a nonlinear Principal Component Analysis (PCA) method, Kernel PCA (KPCA) can effectively extract nonlinear feature. For the object image which includes more nonlinear features, traditional Active Shape Model (ASM) couldn’t obtain a good result of localization. Concerning this, an extending research on nonlinear-ASM is brought here, and an algorithm of object localization based on nonlinear-ASM is proposed. In the research of nonlinear-ASM, the problem of high dimensionality caused by nonlinear mapping has been solved effectively by the kernel theory. Besides, KPCA can not reconstruct the pre-image of the input space, thus prior model is hardly constructed by the method of the nonlinear-ASM. For solving this problem, the theory of multi-dimensional scaling is researched in the paper. The validity of the proposed method is demonstrated by the results of experiments.

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Author information

Authors and Affiliations

  1. Information and Network Center, CAUC, Tianjin, China

    Liu Fan

  2. Information Technology Research Base of CAAC, College of Computer Science and Technology of CAUC, Tianjin, China

    Xu Tao & Sun Tong

Authors
  1. Liu Fan
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  2. Xu Tao
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  3. Sun Tong
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Corresponding author

Correspondence to Liu Fan .

Editor information

Editors and Affiliations

  1. Information Engineering Research Institu, Continental Dr 100, Newark, 19713, USA

    Gary Lee

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© 2012 Springer-Verlag GmbH Berlin Heidelberg

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Fan, L., Tao, X., Tong, S. (2012). Kernel PCA and Nonlinear ASM. In: Lee, G. (eds) Advances in Intelligent Systems. Advances in Intelligent and Soft Computing, vol 138. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27869-3_37

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  • DOI: https://doi.org/10.1007/978-3-642-27869-3_37

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-27868-6

  • Online ISBN: 978-3-642-27869-3

  • eBook Packages: EngineeringEngineering (R0)

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