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Minimum Description Length Shape Model Based on Elliptic Fourier Descriptors

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3972))

Abstract

This paper provides the construction of statistical shape model based on elliptic Fourier transformation and minimum description length (MDL). The method does not require manual identification of landmarks on training shapes. Each training shapes can be decomposed into a set of ellipse by elliptic Fourier transformation at a different frequency level. The MDL objective function is based on elliptic Fourier descriptors and principal component analysis (EF-PCA). Experiments show that our method can get better models.

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References

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© 2006 Springer-Verlag Berlin Heidelberg

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Wang, S., Qi, F., Li, H. (2006). Minimum Description Length Shape Model Based on Elliptic Fourier Descriptors. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3972. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760023_95

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  • DOI: https://doi.org/10.1007/11760023_95

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34437-7

  • Online ISBN: 978-3-540-34438-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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