IEICE TRANSACTIONS on Information
3-D Motion Estimation from Optical Flow with Low Computational Cost and Small Variance
Summary :
In this paper, we study three-dimensional motion estimation using optical flow. We construct a weighted quotient-form objective function that provides an unbiased estimator. Using this objective function with a certain projection operator as a weight drastically reduces the computational cost for estimation compared with using the maximum likelihood estimator. To reduce the variance of the estimator, we examine the weight, and we show by theoretical evaluations and simulations that, with an appropriate projection function, and when the noise variance is not too small, this objective function provides an estimator whose variance is smaller than that of the maximum likelihood estimator. The use of this projection is based on the knowledge that the depth function has a positive value (i. e., the object is in front of the camera) and that it is generally smooth.
- Publication
- IEICE TRANSACTIONS on Information Vol.E79-D No.3 pp.230-241
- Publication Date
- 1996/03/25
- 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, "3-D Motion Estimation from Optical Flow with Low Computational Cost and Small Variance" in IEICE TRANSACTIONS on Information,
vol. E79-D, no. 3, pp. 230-241, March 1996, doi: .
Abstract: In this paper, we study three-dimensional motion estimation using optical flow. We construct a weighted quotient-form objective function that provides an unbiased estimator. Using this objective function with a certain projection operator as a weight drastically reduces the computational cost for estimation compared with using the maximum likelihood estimator. To reduce the variance of the estimator, we examine the weight, and we show by theoretical evaluations and simulations that, with an appropriate projection function, and when the noise variance is not too small, this objective function provides an estimator whose variance is smaller than that of the maximum likelihood estimator. The use of this projection is based on the knowledge that the depth function has a positive value (i. e., the object is in front of the camera) and that it is generally smooth.
URL: https://global.ieice.org/en_transactions/information/10.1587/e79-d_3_230/_p
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@ARTICLE{e79-d_3_230,
author={Norio TAGAWA, Takashi TORIU, Toshio ENDOH, },
journal={IEICE TRANSACTIONS on Information},
title={3-D Motion Estimation from Optical Flow with Low Computational Cost and Small Variance},
year={1996},
volume={E79-D},
number={3},
pages={230-241},
abstract={In this paper, we study three-dimensional motion estimation using optical flow. We construct a weighted quotient-form objective function that provides an unbiased estimator. Using this objective function with a certain projection operator as a weight drastically reduces the computational cost for estimation compared with using the maximum likelihood estimator. To reduce the variance of the estimator, we examine the weight, and we show by theoretical evaluations and simulations that, with an appropriate projection function, and when the noise variance is not too small, this objective function provides an estimator whose variance is smaller than that of the maximum likelihood estimator. The use of this projection is based on the knowledge that the depth function has a positive value (i. e., the object is in front of the camera) and that it is generally smooth.},
keywords={},
doi={},
ISSN={},
month={March},}
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TY - JOUR
TI - 3-D Motion Estimation from Optical Flow with Low Computational Cost and Small Variance
T2 - IEICE TRANSACTIONS on Information
SP - 230
EP - 241
AU - Norio TAGAWA
AU - Takashi TORIU
AU - Toshio ENDOH
PY - 1996
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E79-D
IS - 3
JA - IEICE TRANSACTIONS on Information
Y1 - March 1996
AB - In this paper, we study three-dimensional motion estimation using optical flow. We construct a weighted quotient-form objective function that provides an unbiased estimator. Using this objective function with a certain projection operator as a weight drastically reduces the computational cost for estimation compared with using the maximum likelihood estimator. To reduce the variance of the estimator, we examine the weight, and we show by theoretical evaluations and simulations that, with an appropriate projection function, and when the noise variance is not too small, this objective function provides an estimator whose variance is smaller than that of the maximum likelihood estimator. The use of this projection is based on the knowledge that the depth function has a positive value (i. e., the object is in front of the camera) and that it is generally smooth.
ER -
English
