A new fast and robust template matching with randomness

Chang Liu; Yongqiang Bai

doi:10.1109/CCDC.2017.7978674

A new fast and robust template matching with randomness

Chang Liu
, Yongqiang Bai

School of Automation

Beijing Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

Abstract

Template matching is one of the most important techniques in computer vision, where the algorithm should find the location of template image in scene image. The commonly used method of template matching is Normalized Cross Correlation which has a high matching accuracy while consuming a large amount of computational speed. In this paper, a novel, fast and robust template matching approach is proposed. The new algorithm randomly visits the pixels and locates local maxima by gradually moving to the regions with larger NCC values. To further improve the speed and accuracy of the algorithm, several additional rules are established. Theoretical analysis and experimental results show that the proposed algorithm maintain a high matching accuracy while providing a significant speedup.

Original language	English
Title of host publication	Proceedings of the 29th Chinese Control and Decision Conference, CCDC 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1053-1058
Number of pages	6
ISBN (Electronic)	9781509046560
DOIs	https://doi.org/10.1109/CCDC.2017.7978674
Publication status	Published - 12 Jul 2017
Event	29th Chinese Control and Decision Conference, CCDC 2017 - Chongqing, China Duration: 28 May 2017 → 30 May 2017

Publication series

Name	Proceedings of the 29th Chinese Control and Decision Conference, CCDC 2017

Conference

Conference	29th Chinese Control and Decision Conference, CCDC 2017
Country/Territory	China
City	Chongqing
Period	28/05/17 → 30/05/17

Keywords

Brightness-contrast Invariance
Normalized Cross Correlation
Template Matching

Access to Document

10.1109/CCDC.2017.7978674

Cite this

Liu, C., & Bai, Y. (2017). A new fast and robust template matching with randomness. In Proceedings of the 29th Chinese Control and Decision Conference, CCDC 2017 (pp. 1053-1058). Article 7978674 (Proceedings of the 29th Chinese Control and Decision Conference, CCDC 2017). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CCDC.2017.7978674

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title = "A new fast and robust template matching with randomness",

abstract = "Template matching is one of the most important techniques in computer vision, where the algorithm should find the location of template image in scene image. The commonly used method of template matching is Normalized Cross Correlation which has a high matching accuracy while consuming a large amount of computational speed. In this paper, a novel, fast and robust template matching approach is proposed. The new algorithm randomly visits the pixels and locates local maxima by gradually moving to the regions with larger NCC values. To further improve the speed and accuracy of the algorithm, several additional rules are established. Theoretical analysis and experimental results show that the proposed algorithm maintain a high matching accuracy while providing a significant speedup.",

keywords = "Brightness-contrast Invariance, Normalized Cross Correlation, Template Matching",

author = "Chang Liu and Yongqiang Bai",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 29th Chinese Control and Decision Conference, CCDC 2017 ; Conference date: 28-05-2017 Through 30-05-2017",

year = "2017",

month = jul,

day = "12",

doi = "10.1109/CCDC.2017.7978674",

language = "English",

series = "Proceedings of the 29th Chinese Control and Decision Conference, CCDC 2017",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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booktitle = "Proceedings of the 29th Chinese Control and Decision Conference, CCDC 2017",

address = "United States",

}

Liu, C & Bai, Y 2017, A new fast and robust template matching with randomness. in Proceedings of the 29th Chinese Control and Decision Conference, CCDC 2017., 7978674, Proceedings of the 29th Chinese Control and Decision Conference, CCDC 2017, Institute of Electrical and Electronics Engineers Inc., pp. 1053-1058, 29th Chinese Control and Decision Conference, CCDC 2017, Chongqing, China, 28/05/17. https://doi.org/10.1109/CCDC.2017.7978674

A new fast and robust template matching with randomness. / Liu, Chang; Bai, Yongqiang.
Proceedings of the 29th Chinese Control and Decision Conference, CCDC 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 1053-1058 7978674 (Proceedings of the 29th Chinese Control and Decision Conference, CCDC 2017).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - A new fast and robust template matching with randomness

AU - Liu, Chang

AU - Bai, Yongqiang

PY - 2017/7/12

Y1 - 2017/7/12

N2 - Template matching is one of the most important techniques in computer vision, where the algorithm should find the location of template image in scene image. The commonly used method of template matching is Normalized Cross Correlation which has a high matching accuracy while consuming a large amount of computational speed. In this paper, a novel, fast and robust template matching approach is proposed. The new algorithm randomly visits the pixels and locates local maxima by gradually moving to the regions with larger NCC values. To further improve the speed and accuracy of the algorithm, several additional rules are established. Theoretical analysis and experimental results show that the proposed algorithm maintain a high matching accuracy while providing a significant speedup.

AB - Template matching is one of the most important techniques in computer vision, where the algorithm should find the location of template image in scene image. The commonly used method of template matching is Normalized Cross Correlation which has a high matching accuracy while consuming a large amount of computational speed. In this paper, a novel, fast and robust template matching approach is proposed. The new algorithm randomly visits the pixels and locates local maxima by gradually moving to the regions with larger NCC values. To further improve the speed and accuracy of the algorithm, several additional rules are established. Theoretical analysis and experimental results show that the proposed algorithm maintain a high matching accuracy while providing a significant speedup.

KW - Brightness-contrast Invariance

KW - Normalized Cross Correlation

KW - Template Matching

UR - https://www.scopus.com/pages/publications/85028049504

U2 - 10.1109/CCDC.2017.7978674

DO - 10.1109/CCDC.2017.7978674

M3 - Conference contribution

AN - SCOPUS:85028049504

T3 - Proceedings of the 29th Chinese Control and Decision Conference, CCDC 2017

SP - 1053

EP - 1058

BT - Proceedings of the 29th Chinese Control and Decision Conference, CCDC 2017

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 29th Chinese Control and Decision Conference, CCDC 2017

Y2 - 28 May 2017 through 30 May 2017

ER -

A new fast and robust template matching with randomness

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Keywords

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