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Optimal Estimation

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Computer Vision

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Synonyms

Optimal parameter estimation

Related Concepts

Maximum Likelihood Estimation Ellipse Fitting Fundamental Matrix Homography KCR Lower Bound Hyper-Renormalization

Definition

Optimal estimation in the computer vision context refers to estimating the parameters that describe the underlying problem from noisy observation. The estimation is done according to a given criterion of optimality, for which maximum likelihood is widely accepted. If Gaussian noise is assumed, it reduces to minimizing the Mahalanobis distance. If furthermore the Gaussian noise has a homogeneous and isotropic distribution, the procedure reduces to minimizing what is called the reprojection error.

Background

One of the central tasks of computer vision is the extraction of 2D/3D geometric information from noisy image data. Here, the term image data refers to values extracted from images by image processing operations such as edge filters and interest point detectors. Image data are said to be noisyin the sense...

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References

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Author information

Authors and Affiliations

  1. Professor Emeritus, Okayama University, Okayama, Japan

    Kenichi Kanatani

Authors
  1. Kenichi Kanatani
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Corresponding author

Correspondence to Kenichi Kanatani .

Section Editor information

  1. Tohoku University, Sendai, Japan

    Koichiro Deguchi

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Kanatani, K. (2020). Optimal Estimation. In: Computer Vision. Springer, Cham. https://doi.org/10.1007/978-3-030-03243-2_714-1

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  • DOI: https://doi.org/10.1007/978-3-030-03243-2_714-1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-03243-2

  • Online ISBN: 978-3-030-03243-2

  • eBook Packages: Springer Reference Computer SciencesReference Module Computer Science and Engineering

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