IEICE TRANSACTIONS on Information
Estimation of Dense Displacement by Scale Invariant Polynomial Expansion of Heterogeneous Multi-View Images
Summary :
Several applications for 3-D visualization require dense detection of correspondence for displacement estimation among heterogeneous multi-view images. Due to differences in resolution or sampling density and field of view in the images, estimation of dense displacement is not straight forward. Therefore, we propose a scale invariant polynomial expansion method that can estimate dense displacement between two heterogeneous views. Evaluation on heterogeneous images verifies accuracy of our approach.
- Publication
- IEICE TRANSACTIONS on Information Vol.E100-D No.9 pp.2048-2051
- Publication Date
- 2017/09/01
- Publicized
- 2017/06/14
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2016PCL0008
- Type of Manuscript
- Special Section LETTER (Special Section on Picture Coding and Image Media Processing)
- Category
Authors
Kazuki SHIBATA
Nagoya University
Mehrdad PANAHPOUR TEHERANI
Nagoya University
Keita TAKAHASHI
Nagoya University
Toshiaki FUJII
Nagoya University
Keyword
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Kazuki SHIBATA, Mehrdad PANAHPOUR TEHERANI, Keita TAKAHASHI, Toshiaki FUJII, "Estimation of Dense Displacement by Scale Invariant Polynomial Expansion of Heterogeneous Multi-View Images" in IEICE TRANSACTIONS on Information,
vol. E100-D, no. 9, pp. 2048-2051, September 2017, doi: 10.1587/transinf.2016PCL0008.
Abstract: Several applications for 3-D visualization require dense detection of correspondence for displacement estimation among heterogeneous multi-view images. Due to differences in resolution or sampling density and field of view in the images, estimation of dense displacement is not straight forward. Therefore, we propose a scale invariant polynomial expansion method that can estimate dense displacement between two heterogeneous views. Evaluation on heterogeneous images verifies accuracy of our approach.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2016PCL0008/_p
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@ARTICLE{e100-d_9_2048,
author={Kazuki SHIBATA, Mehrdad PANAHPOUR TEHERANI, Keita TAKAHASHI, Toshiaki FUJII, },
journal={IEICE TRANSACTIONS on Information},
title={Estimation of Dense Displacement by Scale Invariant Polynomial Expansion of Heterogeneous Multi-View Images},
year={2017},
volume={E100-D},
number={9},
pages={2048-2051},
abstract={Several applications for 3-D visualization require dense detection of correspondence for displacement estimation among heterogeneous multi-view images. Due to differences in resolution or sampling density and field of view in the images, estimation of dense displacement is not straight forward. Therefore, we propose a scale invariant polynomial expansion method that can estimate dense displacement between two heterogeneous views. Evaluation on heterogeneous images verifies accuracy of our approach.},
keywords={},
doi={10.1587/transinf.2016PCL0008},
ISSN={1745-1361},
month={September},}
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TY - JOUR
TI - Estimation of Dense Displacement by Scale Invariant Polynomial Expansion of Heterogeneous Multi-View Images
T2 - IEICE TRANSACTIONS on Information
SP - 2048
EP - 2051
AU - Kazuki SHIBATA
AU - Mehrdad PANAHPOUR TEHERANI
AU - Keita TAKAHASHI
AU - Toshiaki FUJII
PY - 2017
DO - 10.1587/transinf.2016PCL0008
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E100-D
IS - 9
JA - IEICE TRANSACTIONS on Information
Y1 - September 2017
AB - Several applications for 3-D visualization require dense detection of correspondence for displacement estimation among heterogeneous multi-view images. Due to differences in resolution or sampling density and field of view in the images, estimation of dense displacement is not straight forward. Therefore, we propose a scale invariant polynomial expansion method that can estimate dense displacement between two heterogeneous views. Evaluation on heterogeneous images verifies accuracy of our approach.
ER -
English
