IEICE TRANSACTIONS on Information
Detecting Multiple Rigid Image Motions from an Optical Flow Field Obtained with Multi-Scale, Multi-Orientation Filters
Summary :
A method for detecting multiple rigid motions in images from an optical flow field obtained with multi-scale, multi-orientation filters is proposed. Convolving consecutive gray scale images with a set of eight orientation-selective spatial Gaussian filters yields eight gradient constraint equations for the two components of a flow vector at every location. The flow vector and an uncertainty measure are obtained from these equations. In the neighborhood of motion boundary, the uncertainty of the flow vectors increase. By using multiple sets of filters of different scales, multiple flow vectors are obtained at every location, from which the one with minimal uncertainty measure is selected. The obtained flow field is then segmented in order to solve the aperture problem and to remove noise without blurring discontinuity in the flow field. Discontinuities are first detected as those locations where flow vectors have relatively larger uncertainty measures. Then similar flow vectors are gouped into regions. By modeling flow vectors, regions are merged to form segments each of which belongs to a planar patch of a rigid object in the scene.
- Publication
- IEICE TRANSACTIONS on Information Vol.E76-D No.10 pp.1253-1262
- Publication Date
- 1993/10/25
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- PAPER
- Category
- Image Processing, Computer Graphics and Pattern Recognition
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
Hsiao-Jing CHEN, Yoshiaki SHIRAI, Minoru ASADA, "Detecting Multiple Rigid Image Motions from an Optical Flow Field Obtained with Multi-Scale, Multi-Orientation Filters" in IEICE TRANSACTIONS on Information,
vol. E76-D, no. 10, pp. 1253-1262, October 1993, doi: .
Abstract: A method for detecting multiple rigid motions in images from an optical flow field obtained with multi-scale, multi-orientation filters is proposed. Convolving consecutive gray scale images with a set of eight orientation-selective spatial Gaussian filters yields eight gradient constraint equations for the two components of a flow vector at every location. The flow vector and an uncertainty measure are obtained from these equations. In the neighborhood of motion boundary, the uncertainty of the flow vectors increase. By using multiple sets of filters of different scales, multiple flow vectors are obtained at every location, from which the one with minimal uncertainty measure is selected. The obtained flow field is then segmented in order to solve the aperture problem and to remove noise without blurring discontinuity in the flow field. Discontinuities are first detected as those locations where flow vectors have relatively larger uncertainty measures. Then similar flow vectors are gouped into regions. By modeling flow vectors, regions are merged to form segments each of which belongs to a planar patch of a rigid object in the scene.
URL: https://global.ieice.org/en_transactions/information/10.1587/e76-d_10_1253/_p
Copy
@ARTICLE{e76-d_10_1253,
author={Hsiao-Jing CHEN, Yoshiaki SHIRAI, Minoru ASADA, },
journal={IEICE TRANSACTIONS on Information},
title={Detecting Multiple Rigid Image Motions from an Optical Flow Field Obtained with Multi-Scale, Multi-Orientation Filters},
year={1993},
volume={E76-D},
number={10},
pages={1253-1262},
abstract={A method for detecting multiple rigid motions in images from an optical flow field obtained with multi-scale, multi-orientation filters is proposed. Convolving consecutive gray scale images with a set of eight orientation-selective spatial Gaussian filters yields eight gradient constraint equations for the two components of a flow vector at every location. The flow vector and an uncertainty measure are obtained from these equations. In the neighborhood of motion boundary, the uncertainty of the flow vectors increase. By using multiple sets of filters of different scales, multiple flow vectors are obtained at every location, from which the one with minimal uncertainty measure is selected. The obtained flow field is then segmented in order to solve the aperture problem and to remove noise without blurring discontinuity in the flow field. Discontinuities are first detected as those locations where flow vectors have relatively larger uncertainty measures. Then similar flow vectors are gouped into regions. By modeling flow vectors, regions are merged to form segments each of which belongs to a planar patch of a rigid object in the scene.},
keywords={},
doi={},
ISSN={},
month={October},}
Copy
TY - JOUR
TI - Detecting Multiple Rigid Image Motions from an Optical Flow Field Obtained with Multi-Scale, Multi-Orientation Filters
T2 - IEICE TRANSACTIONS on Information
SP - 1253
EP - 1262
AU - Hsiao-Jing CHEN
AU - Yoshiaki SHIRAI
AU - Minoru ASADA
PY - 1993
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E76-D
IS - 10
JA - IEICE TRANSACTIONS on Information
Y1 - October 1993
AB - A method for detecting multiple rigid motions in images from an optical flow field obtained with multi-scale, multi-orientation filters is proposed. Convolving consecutive gray scale images with a set of eight orientation-selective spatial Gaussian filters yields eight gradient constraint equations for the two components of a flow vector at every location. The flow vector and an uncertainty measure are obtained from these equations. In the neighborhood of motion boundary, the uncertainty of the flow vectors increase. By using multiple sets of filters of different scales, multiple flow vectors are obtained at every location, from which the one with minimal uncertainty measure is selected. The obtained flow field is then segmented in order to solve the aperture problem and to remove noise without blurring discontinuity in the flow field. Discontinuities are first detected as those locations where flow vectors have relatively larger uncertainty measures. Then similar flow vectors are gouped into regions. By modeling flow vectors, regions are merged to form segments each of which belongs to a planar patch of a rigid object in the scene.
ER -
English
