Non-Linear Extension of Generalized Hyperplane Approximation
Summary :
A non-linear extension of generalized hyperplane approximation (GHA) method is introduced in this letter. Although GHA achieved a high-confidence result in motion parameter estimation by utilizing the supervised learning scheme in histogram of oriented gradient (HOG) feature space, it still has unstable convergence range because it approximates the non-linear function of regression from the feature space to the motion parameter space as a linear plane. To extend GHA into a non-linear regression for larger convergence range, we derive theoretical equations and verify this extension's effectiveness and efficiency over GHA by experimental results.
- Publication
- IEICE TRANSACTIONS on Information Vol.E99-D No.6 pp.1707-1710
- Publication Date
- 2016/06/01
- Publicized
- 2016/02/29
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2015EDL8214
- Type of Manuscript
- LETTER
- Category
- Image Recognition, Computer Vision
Authors
Hyun-Chul CHOI
Yeungnam University
Keyword
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Hyun-Chul CHOI, "Non-Linear Extension of Generalized Hyperplane Approximation" in IEICE TRANSACTIONS on Information,
vol. E99-D, no. 6, pp. 1707-1710, June 2016, doi: 10.1587/transinf.2015EDL8214.
Abstract: A non-linear extension of generalized hyperplane approximation (GHA) method is introduced in this letter. Although GHA achieved a high-confidence result in motion parameter estimation by utilizing the supervised learning scheme in histogram of oriented gradient (HOG) feature space, it still has unstable convergence range because it approximates the non-linear function of regression from the feature space to the motion parameter space as a linear plane. To extend GHA into a non-linear regression for larger convergence range, we derive theoretical equations and verify this extension's effectiveness and efficiency over GHA by experimental results.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2015EDL8214/_p
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@ARTICLE{e99-d_6_1707,
author={Hyun-Chul CHOI, },
journal={IEICE TRANSACTIONS on Information},
title={Non-Linear Extension of Generalized Hyperplane Approximation},
year={2016},
volume={E99-D},
number={6},
pages={1707-1710},
abstract={A non-linear extension of generalized hyperplane approximation (GHA) method is introduced in this letter. Although GHA achieved a high-confidence result in motion parameter estimation by utilizing the supervised learning scheme in histogram of oriented gradient (HOG) feature space, it still has unstable convergence range because it approximates the non-linear function of regression from the feature space to the motion parameter space as a linear plane. To extend GHA into a non-linear regression for larger convergence range, we derive theoretical equations and verify this extension's effectiveness and efficiency over GHA by experimental results.},
keywords={},
doi={10.1587/transinf.2015EDL8214},
ISSN={1745-1361},
month={June},}
Copy
TY - JOUR
TI - Non-Linear Extension of Generalized Hyperplane Approximation
T2 - IEICE TRANSACTIONS on Information
SP - 1707
EP - 1710
AU - Hyun-Chul CHOI
PY - 2016
DO - 10.1587/transinf.2015EDL8214
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E99-D
IS - 6
JA - IEICE TRANSACTIONS on Information
Y1 - June 2016
AB - A non-linear extension of generalized hyperplane approximation (GHA) method is introduced in this letter. Although GHA achieved a high-confidence result in motion parameter estimation by utilizing the supervised learning scheme in histogram of oriented gradient (HOG) feature space, it still has unstable convergence range because it approximates the non-linear function of regression from the feature space to the motion parameter space as a linear plane. To extend GHA into a non-linear regression for larger convergence range, we derive theoretical equations and verify this extension's effectiveness and efficiency over GHA by experimental results.
ER -
English
