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Since the detection of optical flow (two-dimensional motion field on an image) from image sequences is essentially an ill-posed problem, most of the conventional methods use a smoothness constraint for optical flow heuristically and detect reasonable optical flow. However, little discussion exists regarding the degree of smoothness. Furthermore, to recover the relative three-dimensional motion and depth between a camera and a rigid object, in general at first, the optical flow is detected without a rigid motion constraint, and next, the motion and depth are estimated using the detected optical flow. Rigorously speaking, the optical flow should be detected with such a constraint, and consequently three-dimensional motion and depth should be determined. To solve these problems, in this paper, we apply a parametric model to an optical flow, and construct an estimation algorithm based on this model.

- Publication
- IEICE TRANSACTIONS on Information Vol.E84-D No.4 pp.485-494

- Publication Date
- 2001/04/01

- Publicized

- Online ISSN

- DOI

- Type of Manuscript
- PAPER

- Category
- Image Processing, Image Pattern Recognition

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Norio TAGAWA, Atsuya INAGAKI, Akihiro MINAGAWA, "Parametric Estimation of Optical Flow from Two Perspective Views" in IEICE TRANSACTIONS on Information,
vol. E84-D, no. 4, pp. 485-494, April 2001, doi: .

Abstract: Since the detection of optical flow (two-dimensional motion field on an image) from image sequences is essentially an ill-posed problem, most of the conventional methods use a smoothness constraint for optical flow heuristically and detect reasonable optical flow. However, little discussion exists regarding the degree of smoothness. Furthermore, to recover the relative three-dimensional motion and depth between a camera and a rigid object, in general at first, the optical flow is detected without a rigid motion constraint, and next, the motion and depth are estimated using the detected optical flow. Rigorously speaking, the optical flow should be detected with such a constraint, and consequently three-dimensional motion and depth should be determined. To solve these problems, in this paper, we apply a parametric model to an optical flow, and construct an estimation algorithm based on this model.

URL: https://global.ieice.org/en_transactions/information/10.1587/e84-d_4_485/_p

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@ARTICLE{e84-d_4_485,

author={Norio TAGAWA, Atsuya INAGAKI, Akihiro MINAGAWA, },

journal={IEICE TRANSACTIONS on Information},

title={Parametric Estimation of Optical Flow from Two Perspective Views},

year={2001},

volume={E84-D},

number={4},

pages={485-494},

abstract={Since the detection of optical flow (two-dimensional motion field on an image) from image sequences is essentially an ill-posed problem, most of the conventional methods use a smoothness constraint for optical flow heuristically and detect reasonable optical flow. However, little discussion exists regarding the degree of smoothness. Furthermore, to recover the relative three-dimensional motion and depth between a camera and a rigid object, in general at first, the optical flow is detected without a rigid motion constraint, and next, the motion and depth are estimated using the detected optical flow. Rigorously speaking, the optical flow should be detected with such a constraint, and consequently three-dimensional motion and depth should be determined. To solve these problems, in this paper, we apply a parametric model to an optical flow, and construct an estimation algorithm based on this model.},

keywords={},

doi={},

ISSN={},

month={April},}

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TY - JOUR

TI - Parametric Estimation of Optical Flow from Two Perspective Views

T2 - IEICE TRANSACTIONS on Information

SP - 485

EP - 494

AU - Norio TAGAWA

AU - Atsuya INAGAKI

AU - Akihiro MINAGAWA

PY - 2001

DO -

JO - IEICE TRANSACTIONS on Information

SN -

VL - E84-D

IS - 4

JA - IEICE TRANSACTIONS on Information

Y1 - April 2001

AB - Since the detection of optical flow (two-dimensional motion field on an image) from image sequences is essentially an ill-posed problem, most of the conventional methods use a smoothness constraint for optical flow heuristically and detect reasonable optical flow. However, little discussion exists regarding the degree of smoothness. Furthermore, to recover the relative three-dimensional motion and depth between a camera and a rigid object, in general at first, the optical flow is detected without a rigid motion constraint, and next, the motion and depth are estimated using the detected optical flow. Rigorously speaking, the optical flow should be detected with such a constraint, and consequently three-dimensional motion and depth should be determined. To solve these problems, in this paper, we apply a parametric model to an optical flow, and construct an estimation algorithm based on this model.

ER -