Renormalization for Motion Analysis: Statistically Optimal Algorithm

Kenichi KANATANI

Renormalization for Motion Analysis: Statistically Optimal Algorithm

Kenichi KANATANI

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Introducing a general statistical model of image noise, we present an optimal algorithm for computing 3-D motion from two views without involving numerical search: () the essential matrix is computed by a scheme called renormalization; () the decomposability condition is optimally imposed on it so that it exactly decomposes into motion parameters; () image feature points are optimally corrected so that they define their 3-D depths. Our scheme not only produces a statistically optimal solution but also evaluates the reliability of the computed motion parameters and reconstructed points in quantitative terms.

Publication: IEICE TRANSACTIONS on Information Vol.E77-D No.11 pp.1233-1239

Publication Date: 1994/11/25

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Type of Manuscript: Special Section PAPER (Special Issue on Computer Vision)

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Kenichi KANATANI, "Renormalization for Motion Analysis: Statistically Optimal Algorithm" in IEICE TRANSACTIONS on Information, vol. E77-D, no. 11, pp. 1233-1239, November 1994, doi: .
Abstract: Introducing a general statistical model of image noise, we present an optimal algorithm for computing 3-D motion from two views without involving numerical search: () the essential matrix is computed by a scheme called renormalization; () the decomposability condition is optimally imposed on it so that it exactly decomposes into motion parameters; () image feature points are optimally corrected so that they define their 3-D depths. Our scheme not only produces a statistically optimal solution but also evaluates the reliability of the computed motion parameters and reconstructed points in quantitative terms.
URL: https://global.ieice.org/en_transactions/information/10.1587/e77-d_11_1233/_p

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@ARTICLE{e77-d_11_1233,
author={Kenichi KANATANI, },
journal={IEICE TRANSACTIONS on Information},
title={Renormalization for Motion Analysis: Statistically Optimal Algorithm},
year={1994},
volume={E77-D},
number={11},
pages={1233-1239},
abstract={Introducing a general statistical model of image noise, we present an optimal algorithm for computing 3-D motion from two views without involving numerical search: () the essential matrix is computed by a scheme called renormalization; () the decomposability condition is optimally imposed on it so that it exactly decomposes into motion parameters; () image feature points are optimally corrected so that they define their 3-D depths. Our scheme not only produces a statistically optimal solution but also evaluates the reliability of the computed motion parameters and reconstructed points in quantitative terms.},
keywords={},
doi={},
ISSN={},
month={November},}

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TY - JOUR
TI - Renormalization for Motion Analysis: Statistically Optimal Algorithm
T2 - IEICE TRANSACTIONS on Information
SP - 1233
EP - 1239
AU - Kenichi KANATANI
PY - 1994
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E77-D
IS - 11
JA - IEICE TRANSACTIONS on Information
Y1 - November 1994
AB - Introducing a general statistical model of image noise, we present an optimal algorithm for computing 3-D motion from two views without involving numerical search: () the essential matrix is computed by a scheme called renormalization; () the decomposability condition is optimally imposed on it so that it exactly decomposes into motion parameters; () image feature points are optimally corrected so that they define their 3-D depths. Our scheme not only produces a statistically optimal solution but also evaluates the reliability of the computed motion parameters and reconstructed points in quantitative terms.
ER -