Volume 13 Number 5
October 2016
Article Contents
Zhi-Heng Wang, Qin-Feng Song, Hong-Min Liu and Zhan-Qiang Huo. Absence Importance and Its Application to Feature Detection and Matching. International Journal of Automation and Computing, vol. 13, no. 5, pp. 480-490, 2016. doi: 10.1007/s11633-015-0925-7
Cite as: Zhi-Heng Wang, Qin-Feng Song, Hong-Min Liu and Zhan-Qiang Huo. Absence Importance and Its Application to Feature Detection and Matching. International Journal of Automation and Computing, vol. 13, no. 5, pp. 480-490, 2016.

# Absence Importance and Its Application to Feature Detection and Matching

Author Biography:
• ORCID iD: 0000-0002-3241-0720

E-mail: songqf1989@126.com

E-mail: hzq@hpu.edu.cn

• Corresponding author: E-mail: hongminliu@hpu.edu.cn (Corresponding author)
• Accepted: 2014-09-10
• Published Online: 2016-07-25
Fund Project:

National Natural Science Foundation of China 61201395

National Natural Science Foundation of China 61472119

National Natural Science Foundation of China 61472373

the program for Science & Technology Innovation Talents in Universities of Henan Province 13HASTIT039

the Program for Young Backbone Teachers in Universities of Henan Province 2013GGJS-052

the Program for Young Backbone Teachers in Universities of Henan Province 2012GGJS-057

• Feature detection and matching play important roles in many fields of computer vision, such as image understanding, feature recognition, 3D-reconstruction, video analysis, etc. Extracting features is usually the first step for feature detection or matching, and the gradient feature is one of the most used selections. In this paper, a new image feature-absence importance (AI) feature, which can directly characterize the local structure information, is proposed. Greatly different from the most existing features, the proposed absence importance feature is mainly based on the consideration that the absence of the important pixel will have a great effect on the local structure. Two absence importance features, mean absence importance (MAI) and standard deviation absence importance (SDAI), are defined and used subsequently to construct new algorithms for feature detection and matching. Experiments demonstrate that the proposed absence importance features can be used as an important complement of the gradient feature and applied successfully to the fields of feature detection and matching.
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###### 通讯作者: 陈斌, bchen63@163.com
• 1.

沈阳化工大学材料科学与工程学院 沈阳 110142

Figures (12)  / Tables (4)

## Absence Importance and Its Application to Feature Detection and Matching

• ###### Corresponding author:E-mail: hongminliu@hpu.edu.cn (Corresponding author)
Fund Project:

National Natural Science Foundation of China 61201395

National Natural Science Foundation of China 61472119

National Natural Science Foundation of China 61472373

the program for Science & Technology Innovation Talents in Universities of Henan Province 13HASTIT039

the Program for Young Backbone Teachers in Universities of Henan Province 2013GGJS-052

the Program for Young Backbone Teachers in Universities of Henan Province 2012GGJS-057

Abstract: Feature detection and matching play important roles in many fields of computer vision, such as image understanding, feature recognition, 3D-reconstruction, video analysis, etc. Extracting features is usually the first step for feature detection or matching, and the gradient feature is one of the most used selections. In this paper, a new image feature-absence importance (AI) feature, which can directly characterize the local structure information, is proposed. Greatly different from the most existing features, the proposed absence importance feature is mainly based on the consideration that the absence of the important pixel will have a great effect on the local structure. Two absence importance features, mean absence importance (MAI) and standard deviation absence importance (SDAI), are defined and used subsequently to construct new algorithms for feature detection and matching. Experiments demonstrate that the proposed absence importance features can be used as an important complement of the gradient feature and applied successfully to the fields of feature detection and matching.

Zhi-Heng Wang, Qin-Feng Song, Hong-Min Liu and Zhan-Qiang Huo. Absence Importance and Its Application to Feature Detection and Matching. International Journal of Automation and Computing, vol. 13, no. 5, pp. 480-490, 2016. doi: 10.1007/s11633-015-0925-7
 Citation: Zhi-Heng Wang, Qin-Feng Song, Hong-Min Liu and Zhan-Qiang Huo. Absence Importance and Its Application to Feature Detection and Matching. International Journal of Automation and Computing, vol. 13, no. 5, pp. 480-490, 2016.
Reference (35)

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