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Surface Interpolation by Adaptive Neuro-fuzzy Inference System Based Local Ordinary Kriging

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Computer Vision – ACCV 2006 (ACCV 2006)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 3851))

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Abstract

A new approach to the Ordinary Kriging interpolation method based on the combination of local interpolation and variogram modelling with Adaptive Neuro-Fuzzy Inference System (ANFIS) is proposed for surface interpolation. In this method, the experimental variogram is modelled by ANFIS and this model is used to interpolate the unknown values of specific points in a new local manner. In this local way, all the unknown points are grouped based on each reference point. As the study data, two types of data sets coming from mathematical functions and a 3D scanning system are used. The tests show that the proposed method produces better performances for all data sets in comparison to the well known and highly approved interpolation methods; Ordinary Kriging, Triangle Based Cubic and Radial Basis Function-Multiquadric. Moreover, by the proposed method the computational complexity impressively decreases compared to the global Ordinary Kriging.

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Author information

Authors and Affiliations

  1. Institute of Science, Computer Engineering Dept., and Engineering Faculty, Geodesy-Photogrammetry Eng., Dept., Erciyes University, 38039, Kayseri, Turkey

    Coşkun Özkan

Authors
  1. Coşkun Özkan
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Editor information

Editors and Affiliations

  1. International Institute of Information Technology, Center for Visual Information Technology, Hyderabad, India

    P. J. Narayanan

  2. Department of Computer Science, Columbia University, 500 West 120th Street, 10027, New York, NY, USA

    Shree K. Nayar

  3. Microsoft Research Asia, Beijing, P.R. China

    Heung-Yeung Shum

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© 2006 Springer-Verlag Berlin Heidelberg

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Özkan, C. (2006). Surface Interpolation by Adaptive Neuro-fuzzy Inference System Based Local Ordinary Kriging. In: Narayanan, P.J., Nayar, S.K., Shum, HY. (eds) Computer Vision – ACCV 2006. ACCV 2006. Lecture Notes in Computer Science, vol 3851. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11612032_21

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  • DOI: https://doi.org/10.1007/11612032_21

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-31219-2

  • Online ISBN: 978-3-540-32433-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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