IEICE TRANSACTIONS on Information
Optical Flow Detection Using a General Noise Model
Summary :
In the usual optical flow detection, the gradient constraint, which expresses the relationship between the gradient of the image intensity and its motion, is combined with the least-squares criterion. This criterion means assuming that only the time derivative of the image intensity contains noise. In this paper, we assume that all image derivatives contain noise and derive a new optical flow detection technique. Since this method requires the knowledge about the covariance matrix of the noise, we also discuss a method for its estimation. Our experiments show that the proposed method can compute optical flow more accurately than the conventional method.
- Publication
- IEICE TRANSACTIONS on Information Vol.E79-D No.7 pp.951-957
- Publication Date
- 1996/07/25
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- PAPER
- Category
- Image Processing,Computer Graphics and Pattern Recognition
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Naoya OHTA, "Optical Flow Detection Using a General Noise Model" in IEICE TRANSACTIONS on Information,
vol. E79-D, no. 7, pp. 951-957, July 1996, doi: .
Abstract: In the usual optical flow detection, the gradient constraint, which expresses the relationship between the gradient of the image intensity and its motion, is combined with the least-squares criterion. This criterion means assuming that only the time derivative of the image intensity contains noise. In this paper, we assume that all image derivatives contain noise and derive a new optical flow detection technique. Since this method requires the knowledge about the covariance matrix of the noise, we also discuss a method for its estimation. Our experiments show that the proposed method can compute optical flow more accurately than the conventional method.
URL: https://global.ieice.org/en_transactions/information/10.1587/e79-d_7_951/_p
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@ARTICLE{e79-d_7_951,
author={Naoya OHTA, },
journal={IEICE TRANSACTIONS on Information},
title={Optical Flow Detection Using a General Noise Model},
year={1996},
volume={E79-D},
number={7},
pages={951-957},
abstract={In the usual optical flow detection, the gradient constraint, which expresses the relationship between the gradient of the image intensity and its motion, is combined with the least-squares criterion. This criterion means assuming that only the time derivative of the image intensity contains noise. In this paper, we assume that all image derivatives contain noise and derive a new optical flow detection technique. Since this method requires the knowledge about the covariance matrix of the noise, we also discuss a method for its estimation. Our experiments show that the proposed method can compute optical flow more accurately than the conventional method.},
keywords={},
doi={},
ISSN={},
month={July},}
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TY - JOUR
TI - Optical Flow Detection Using a General Noise Model
T2 - IEICE TRANSACTIONS on Information
SP - 951
EP - 957
AU - Naoya OHTA
PY - 1996
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E79-D
IS - 7
JA - IEICE TRANSACTIONS on Information
Y1 - July 1996
AB - In the usual optical flow detection, the gradient constraint, which expresses the relationship between the gradient of the image intensity and its motion, is combined with the least-squares criterion. This criterion means assuming that only the time derivative of the image intensity contains noise. In this paper, we assume that all image derivatives contain noise and derive a new optical flow detection technique. Since this method requires the knowledge about the covariance matrix of the noise, we also discuss a method for its estimation. Our experiments show that the proposed method can compute optical flow more accurately than the conventional method.
ER -
English
