Robust Projective Template Matching

Chao ZHANG; Takuya AKASHI

doi:10.1587/transinf.2016EDP7038

Robust Projective Template Matching

Chao ZHANG, Takuya AKASHI

Full Text Views

0

Cite this

Summary :

In this paper, we address the problem of projective template matching which aims to estimate parameters of projective transformation. Although homography can be estimated by combining key-point-based local features and RANSAC, it can hardly be solved with feature-less images or high outlier rate images. Estimating the projective transformation remains a difficult problem due to high-dimensionality and strong non-convexity. Our approach is to quantize the parameters of projective transformation with binary finite field and search for an appropriate solution as the final result over the discrete sampling set. The benefit is that we can avoid searching among a huge amount of potential candidates. Furthermore, in order to approximate the global optimum more efficiently, we develop a level-wise adaptive sampling (LAS) method under genetic algorithm framework. With LAS, the individuals are uniformly selected from each fitness level and the elite solution finally converges to the global optimum. In the experiment, we compare our method against the popular projective solution and systematically analyse our method. The result shows that our method can provide convincing performance and holds wider application scope.

Publication: IEICE TRANSACTIONS on Information Vol.E99-D No.9 pp.2341-2350

Publication Date: 2016/09/01

Publicized: 2016/06/08

Online ISSN: 1745-1361

DOI: 10.1587/transinf.2016EDP7038

Type of Manuscript: PAPER

Category: Image Recognition, Computer Vision

Authors

Chao ZHANG
Iwate University
Takuya AKASHI
Iwate University

Keyword

projective template matching, binary finite field, level-wise adaptive sampling, homography estimation

Cite this

Copy

Chao ZHANG, Takuya AKASHI, "Robust Projective Template Matching" in IEICE TRANSACTIONS on Information, vol. E99-D, no. 9, pp. 2341-2350, September 2016, doi: 10.1587/transinf.2016EDP7038.
Abstract: In this paper, we address the problem of projective template matching which aims to estimate parameters of projective transformation. Although homography can be estimated by combining key-point-based local features and RANSAC, it can hardly be solved with feature-less images or high outlier rate images. Estimating the projective transformation remains a difficult problem due to high-dimensionality and strong non-convexity. Our approach is to quantize the parameters of projective transformation with binary finite field and search for an appropriate solution as the final result over the discrete sampling set. The benefit is that we can avoid searching among a huge amount of potential candidates. Furthermore, in order to approximate the global optimum more efficiently, we develop a level-wise adaptive sampling (LAS) method under genetic algorithm framework. With LAS, the individuals are uniformly selected from each fitness level and the elite solution finally converges to the global optimum. In the experiment, we compare our method against the popular projective solution and systematically analyse our method. The result shows that our method can provide convincing performance and holds wider application scope.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2016EDP7038/_p

Copy

@ARTICLE{e99-d_9_2341,
author={Chao ZHANG, Takuya AKASHI, },
journal={IEICE TRANSACTIONS on Information},
title={Robust Projective Template Matching},
year={2016},
volume={E99-D},
number={9},
pages={2341-2350},
abstract={In this paper, we address the problem of projective template matching which aims to estimate parameters of projective transformation. Although homography can be estimated by combining key-point-based local features and RANSAC, it can hardly be solved with feature-less images or high outlier rate images. Estimating the projective transformation remains a difficult problem due to high-dimensionality and strong non-convexity. Our approach is to quantize the parameters of projective transformation with binary finite field and search for an appropriate solution as the final result over the discrete sampling set. The benefit is that we can avoid searching among a huge amount of potential candidates. Furthermore, in order to approximate the global optimum more efficiently, we develop a level-wise adaptive sampling (LAS) method under genetic algorithm framework. With LAS, the individuals are uniformly selected from each fitness level and the elite solution finally converges to the global optimum. In the experiment, we compare our method against the popular projective solution and systematically analyse our method. The result shows that our method can provide convincing performance and holds wider application scope.},
keywords={},
doi={10.1587/transinf.2016EDP7038},
ISSN={1745-1361},
month={September},}

Copy

TY - JOUR
TI - Robust Projective Template Matching
T2 - IEICE TRANSACTIONS on Information
SP - 2341
EP - 2350
AU - Chao ZHANG
AU - Takuya AKASHI
PY - 2016
DO - 10.1587/transinf.2016EDP7038
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 projective template matching which aims to estimate parameters of projective transformation. Although homography can be estimated by combining key-point-based local features and RANSAC, it can hardly be solved with feature-less images or high outlier rate images. Estimating the projective transformation remains a difficult problem due to high-dimensionality and strong non-convexity. Our approach is to quantize the parameters of projective transformation with binary finite field and search for an appropriate solution as the final result over the discrete sampling set. The benefit is that we can avoid searching among a huge amount of potential candidates. Furthermore, in order to approximate the global optimum more efficiently, we develop a level-wise adaptive sampling (LAS) method under genetic algorithm framework. With LAS, the individuals are uniformly selected from each fitness level and the elite solution finally converges to the global optimum. In the experiment, we compare our method against the popular projective solution and systematically analyse our method. The result shows that our method can provide convincing performance and holds wider application scope.
ER -