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Estimating the size of an object captured with error

Author

Listed:
  • Safet Hamedović

    (University of Zenica)

  • Mirta Benšić

    (Josip Juraj Strossmayer University Osijek)

  • Kristian Sabo

    (Josip Juraj Strossmayer University Osijek)

  • Petar Taler

    (Josip Juraj Strossmayer University Osijek)

Abstract

In many applications we are faced with the problem of estimating object dimensions from a noisy image. Some devices like a fluorescent microscope, X-ray or ultrasound machines, etc., produce imperfect images. Image noise comes from a variety of sources. It can be produced by the physical processes of imaging, or may be caused by the presence of some unwanted structures (e.g. soft tissue captured in images of bones). In the proposed models we suppose that the data are drawn from uniform distribution on the object of interest, but contaminated by an additive error. Here we use two one-dimensional parametric models to construct confidence intervals and statistical tests pertaining to the object size and suggest the possibility of application in two-dimensional problems. Normal and Laplace distributions are used as error distributions. Finally, we illustrate ability of the R-programs we created for these problems on a real-world example.

Suggested Citation

  • Safet Hamedović & Mirta Benšić & Kristian Sabo & Petar Taler, 2018. "Estimating the size of an object captured with error," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 26(3), pages 771-781, September.
  • Handle: RePEc:spr:cejnor:v:26:y:2018:i:3:d:10.1007_s10100-017-0504-9
    DOI: 10.1007/s10100-017-0504-9
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    References listed on IDEAS

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    1. Meister, Alexander, 2006. "Estimating the support of multivariate densities under measurement error," Journal of Multivariate Analysis, Elsevier, vol. 97(8), pages 1702-1717, September.
    2. Bensic, Mirta & Sabo, Kristian, 2007. "Estimating the width of a uniform distribution when data are measured with additive normal errors with known variance," Computational Statistics & Data Analysis, Elsevier, vol. 51(9), pages 4731-4741, May.
    3. KNEIP, Alois & SIMAR, Léopold, 1995. "A General Framework for Frontier Estimation with Panel Data," LIDAM Discussion Papers CORE 1995060, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
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    Cited by:

    1. Marijana Zekić-Sušac & Rudolf Scitovski & Goran Lešaja, 2018. "CEJOR special issue of Croatian Operational Research Society," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 26(3), pages 531-534, September.

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