IEICE TRANSACTIONS on Information
Estimation of 3-D Motion from Optical Flow with Unbiased Objective Function
Summary :
This paper describes a noise resistant algorithm for estimating 3-D rigid motion from optical flow. We first discuss the problem of constructing the objective function to be minimized. If a Gaussian distribution is assumed for the niose, it is well-known that the least-squares minimization becomes the maximum likelihood estimation. However, the use of this objective function makes the minimization procedure more expensive because the program has to go through all the points in the image at each iteration. We therefore introduce an objective function that provides unbiased estimators. Using this function reduces computational costs. Furthermore, since good approximations can be analytically obtained for the function, using them as an initial guess we can apply an iterative minimization method to the function, which is expected to be stable. The effectiveness of this method is demonstrated by computer simulation.
- Publication
- IEICE TRANSACTIONS on Information Vol.E77-D No.10 pp.1148-1161
- Publication Date
- 1994/10/25
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- PAPER
- Category
- Image Processing, Computer Graphics and Pattern Recognition
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Norio TAGAWA, Takashi TORIU, Toshio ENDOH, "Estimation of 3-D Motion from Optical Flow with Unbiased Objective Function" in IEICE TRANSACTIONS on Information,
vol. E77-D, no. 10, pp. 1148-1161, October 1994, doi: .
Abstract: This paper describes a noise resistant algorithm for estimating 3-D rigid motion from optical flow. We first discuss the problem of constructing the objective function to be minimized. If a Gaussian distribution is assumed for the niose, it is well-known that the least-squares minimization becomes the maximum likelihood estimation. However, the use of this objective function makes the minimization procedure more expensive because the program has to go through all the points in the image at each iteration. We therefore introduce an objective function that provides unbiased estimators. Using this function reduces computational costs. Furthermore, since good approximations can be analytically obtained for the function, using them as an initial guess we can apply an iterative minimization method to the function, which is expected to be stable. The effectiveness of this method is demonstrated by computer simulation.
URL: https://global.ieice.org/en_transactions/information/10.1587/e77-d_10_1148/_p
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@ARTICLE{e77-d_10_1148,
author={Norio TAGAWA, Takashi TORIU, Toshio ENDOH, },
journal={IEICE TRANSACTIONS on Information},
title={Estimation of 3-D Motion from Optical Flow with Unbiased Objective Function},
year={1994},
volume={E77-D},
number={10},
pages={1148-1161},
abstract={This paper describes a noise resistant algorithm for estimating 3-D rigid motion from optical flow. We first discuss the problem of constructing the objective function to be minimized. If a Gaussian distribution is assumed for the niose, it is well-known that the least-squares minimization becomes the maximum likelihood estimation. However, the use of this objective function makes the minimization procedure more expensive because the program has to go through all the points in the image at each iteration. We therefore introduce an objective function that provides unbiased estimators. Using this function reduces computational costs. Furthermore, since good approximations can be analytically obtained for the function, using them as an initial guess we can apply an iterative minimization method to the function, which is expected to be stable. The effectiveness of this method is demonstrated by computer simulation.},
keywords={},
doi={},
ISSN={},
month={October},}
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TY - JOUR
TI - Estimation of 3-D Motion from Optical Flow with Unbiased Objective Function
T2 - IEICE TRANSACTIONS on Information
SP - 1148
EP - 1161
AU - Norio TAGAWA
AU - Takashi TORIU
AU - Toshio ENDOH
PY - 1994
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E77-D
IS - 10
JA - IEICE TRANSACTIONS on Information
Y1 - October 1994
AB - This paper describes a noise resistant algorithm for estimating 3-D rigid motion from optical flow. We first discuss the problem of constructing the objective function to be minimized. If a Gaussian distribution is assumed for the niose, it is well-known that the least-squares minimization becomes the maximum likelihood estimation. However, the use of this objective function makes the minimization procedure more expensive because the program has to go through all the points in the image at each iteration. We therefore introduce an objective function that provides unbiased estimators. Using this function reduces computational costs. Furthermore, since good approximations can be analytically obtained for the function, using them as an initial guess we can apply an iterative minimization method to the function, which is expected to be stable. The effectiveness of this method is demonstrated by computer simulation.
ER -
English
