Estimating Motion Parameters Using a Flexible Weight Function
Summary :
In this paper, we propose a method to estimate affine motion parameters from consecutive images with the assumption that the motion in progress can be characterized by an affine model. The motion may be caused either by a moving camera or moving object. The proposed method first extracts motion vectors from a sequence of images and then processes them by adaptive robust estimation to obtain affine parameters. Typically, a robust estimation filters out outliers (velocity vectors that do not fit into the model) by fitting velocity vectors to a predefined model. To filter out potential outliers, our adaptive robust estimation defines a flexible weight function based on a sigmoid function. During the estimation process, we tune the sigmoid function gradually to its hard-limit as the errors between the input data and the estimation model are decreased, so that we can effectively separate non-outliers from outliers with the help of the finally tuned hard-limit form of the weight function. The experimental results show that the suggested approach is very effective in estimating affine parameters.
- Publication
- IEICE TRANSACTIONS on Information Vol.E89-D No.10 pp.2661-2669
- Publication Date
- 2006/10/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1093/ietisy/e89-d.10.2661
- Type of Manuscript
- PAPER
- Category
- Image Recognition, Computer Vision
Authors
Keyword
Latest Issue
Copyrights notice of machine-translated contents
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Cite this
Copy
Seok-Woo JANG, Gye-Young KIM, Hyung-Il CHOI, "Estimating Motion Parameters Using a Flexible Weight Function" in IEICE TRANSACTIONS on Information,
vol. E89-D, no. 10, pp. 2661-2669, October 2006, doi: 10.1093/ietisy/e89-d.10.2661.
Abstract: In this paper, we propose a method to estimate affine motion parameters from consecutive images with the assumption that the motion in progress can be characterized by an affine model. The motion may be caused either by a moving camera or moving object. The proposed method first extracts motion vectors from a sequence of images and then processes them by adaptive robust estimation to obtain affine parameters. Typically, a robust estimation filters out outliers (velocity vectors that do not fit into the model) by fitting velocity vectors to a predefined model. To filter out potential outliers, our adaptive robust estimation defines a flexible weight function based on a sigmoid function. During the estimation process, we tune the sigmoid function gradually to its hard-limit as the errors between the input data and the estimation model are decreased, so that we can effectively separate non-outliers from outliers with the help of the finally tuned hard-limit form of the weight function. The experimental results show that the suggested approach is very effective in estimating affine parameters.
URL: https://global.ieice.org/en_transactions/information/10.1093/ietisy/e89-d.10.2661/_p
Copy
@ARTICLE{e89-d_10_2661,
author={Seok-Woo JANG, Gye-Young KIM, Hyung-Il CHOI, },
journal={IEICE TRANSACTIONS on Information},
title={Estimating Motion Parameters Using a Flexible Weight Function},
year={2006},
volume={E89-D},
number={10},
pages={2661-2669},
abstract={In this paper, we propose a method to estimate affine motion parameters from consecutive images with the assumption that the motion in progress can be characterized by an affine model. The motion may be caused either by a moving camera or moving object. The proposed method first extracts motion vectors from a sequence of images and then processes them by adaptive robust estimation to obtain affine parameters. Typically, a robust estimation filters out outliers (velocity vectors that do not fit into the model) by fitting velocity vectors to a predefined model. To filter out potential outliers, our adaptive robust estimation defines a flexible weight function based on a sigmoid function. During the estimation process, we tune the sigmoid function gradually to its hard-limit as the errors between the input data and the estimation model are decreased, so that we can effectively separate non-outliers from outliers with the help of the finally tuned hard-limit form of the weight function. The experimental results show that the suggested approach is very effective in estimating affine parameters.},
keywords={},
doi={10.1093/ietisy/e89-d.10.2661},
ISSN={1745-1361},
month={October},}
Copy
TY - JOUR
TI - Estimating Motion Parameters Using a Flexible Weight Function
T2 - IEICE TRANSACTIONS on Information
SP - 2661
EP - 2669
AU - Seok-Woo JANG
AU - Gye-Young KIM
AU - Hyung-Il CHOI
PY - 2006
DO - 10.1093/ietisy/e89-d.10.2661
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E89-D
IS - 10
JA - IEICE TRANSACTIONS on Information
Y1 - October 2006
AB - In this paper, we propose a method to estimate affine motion parameters from consecutive images with the assumption that the motion in progress can be characterized by an affine model. The motion may be caused either by a moving camera or moving object. The proposed method first extracts motion vectors from a sequence of images and then processes them by adaptive robust estimation to obtain affine parameters. Typically, a robust estimation filters out outliers (velocity vectors that do not fit into the model) by fitting velocity vectors to a predefined model. To filter out potential outliers, our adaptive robust estimation defines a flexible weight function based on a sigmoid function. During the estimation process, we tune the sigmoid function gradually to its hard-limit as the errors between the input data and the estimation model are decreased, so that we can effectively separate non-outliers from outliers with the help of the finally tuned hard-limit form of the weight function. The experimental results show that the suggested approach is very effective in estimating affine parameters.
ER -
English
