IEICE TRANSACTIONS on Information
Robust Non-Parametric Template Matching with Local Rigidity Constraints
Summary :
In this paper, we address the problem of non-parametric template matching which does not assume any specific deformation models. In real-world matching scenarios, deformation between a template and a matching result usually appears to be non-rigid and non-linear. We propose a novel approach called local rigidity constraints (LRC). LRC is built based on an assumption that the local rigidity, which is referred to as structural persistence between image patches, can help the algorithm to achieve better performance. A spatial relation test is proposed to weight the rigidity between two image patches. When estimating visual similarity under an unconstrained environment, high-level similarity (e.g. with complex geometry transformations) can then be estimated by investigating the number of LRC. In the searching step, exhaustive matching is possible because of the simplicity of the algorithm. Global maximum is given out as the final matching result. To evaluate our method, we carry out a comprehensive comparison on a publicly available benchmark and show that our method can outperform the state-of-the-art method.
- Publication
- IEICE TRANSACTIONS on Information Vol.E99-D No.9 pp.2332-2340
- Publication Date
- 2016/09/01
- Publicized
- 2016/06/03
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2015EDP7492
- Type of Manuscript
- PAPER
- Category
- Image Recognition, Computer Vision
Authors
Chao ZHANG
Iwate University
Haitian SUN
Iwate University
Takuya AKASHI
Iwate University
Keyword
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Chao ZHANG, Haitian SUN, Takuya AKASHI, "Robust Non-Parametric Template Matching with Local Rigidity Constraints" in IEICE TRANSACTIONS on Information,
vol. E99-D, no. 9, pp. 2332-2340, September 2016, doi: 10.1587/transinf.2015EDP7492.
Abstract: In this paper, we address the problem of non-parametric template matching which does not assume any specific deformation models. In real-world matching scenarios, deformation between a template and a matching result usually appears to be non-rigid and non-linear. We propose a novel approach called local rigidity constraints (LRC). LRC is built based on an assumption that the local rigidity, which is referred to as structural persistence between image patches, can help the algorithm to achieve better performance. A spatial relation test is proposed to weight the rigidity between two image patches. When estimating visual similarity under an unconstrained environment, high-level similarity (e.g. with complex geometry transformations) can then be estimated by investigating the number of LRC. In the searching step, exhaustive matching is possible because of the simplicity of the algorithm. Global maximum is given out as the final matching result. To evaluate our method, we carry out a comprehensive comparison on a publicly available benchmark and show that our method can outperform the state-of-the-art method.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2015EDP7492/_p
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@ARTICLE{e99-d_9_2332,
author={Chao ZHANG, Haitian SUN, Takuya AKASHI, },
journal={IEICE TRANSACTIONS on Information},
title={Robust Non-Parametric Template Matching with Local Rigidity Constraints},
year={2016},
volume={E99-D},
number={9},
pages={2332-2340},
abstract={In this paper, we address the problem of non-parametric template matching which does not assume any specific deformation models. In real-world matching scenarios, deformation between a template and a matching result usually appears to be non-rigid and non-linear. We propose a novel approach called local rigidity constraints (LRC). LRC is built based on an assumption that the local rigidity, which is referred to as structural persistence between image patches, can help the algorithm to achieve better performance. A spatial relation test is proposed to weight the rigidity between two image patches. When estimating visual similarity under an unconstrained environment, high-level similarity (e.g. with complex geometry transformations) can then be estimated by investigating the number of LRC. In the searching step, exhaustive matching is possible because of the simplicity of the algorithm. Global maximum is given out as the final matching result. To evaluate our method, we carry out a comprehensive comparison on a publicly available benchmark and show that our method can outperform the state-of-the-art method.},
keywords={},
doi={10.1587/transinf.2015EDP7492},
ISSN={1745-1361},
month={September},}
Copy
TY - JOUR
TI - Robust Non-Parametric Template Matching with Local Rigidity Constraints
T2 - IEICE TRANSACTIONS on Information
SP - 2332
EP - 2340
AU - Chao ZHANG
AU - Haitian SUN
AU - Takuya AKASHI
PY - 2016
DO - 10.1587/transinf.2015EDP7492
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E99-D
IS - 9
JA - IEICE TRANSACTIONS on Information
Y1 - September 2016
AB - In this paper, we address the problem of non-parametric template matching which does not assume any specific deformation models. In real-world matching scenarios, deformation between a template and a matching result usually appears to be non-rigid and non-linear. We propose a novel approach called local rigidity constraints (LRC). LRC is built based on an assumption that the local rigidity, which is referred to as structural persistence between image patches, can help the algorithm to achieve better performance. A spatial relation test is proposed to weight the rigidity between two image patches. When estimating visual similarity under an unconstrained environment, high-level similarity (e.g. with complex geometry transformations) can then be estimated by investigating the number of LRC. In the searching step, exhaustive matching is possible because of the simplicity of the algorithm. Global maximum is given out as the final matching result. To evaluate our method, we carry out a comprehensive comparison on a publicly available benchmark and show that our method can outperform the state-of-the-art method.
ER -
English
