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Absence importance and its application to feature detection and matching

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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.

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Author information

Authors and Affiliations

  1. School of Computer Science and Technique, Henan Polytechnic University, Jiaozuo, 454003, China

    Zhi-Heng Wang, Qin-Feng Song, Hong-Min Liu & Zhan-Qiang Huo

Authors
  1. Zhi-Heng Wang
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  2. Qin-Feng Song
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  3. Hong-Min Liu
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  4. Zhan-Qiang Huo
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Corresponding author

Correspondence to Hong-Min Liu.

Additional information

This work was supported by National Natural Science Foundation of China (Nos. 61201395, 61272394, 61472119 and 61472373), the program for Science & Technology Innovation Talents in Universities of Henan Province (No. 13HASTIT039) and the Program for Young Backbone Teachers in Universities of Henan Province (Nos. 2012GGJS-057 and 2013GGJS-052).

Recommended by Associate Editor De Xu

Zhi-Heng Wang received the B. Sc. degree in mechatronic engineering from Beijing Institute of Technology, China in 2004, and the Ph.D. degree from Institute of Automation, Chinese Academy of Sciences, China in 2009. Currently, he is an associate professor at School of Computer Science and Technique, Henan Polytechnic University, China.

His research interests include computer vision, pattern recognition, and image processing.

ORCID iD: 0000-0002-3241-0720

Qin-Feng Song received the Bachelor degree from Henan Polytechnic University, China in 2012. Currently, he is a master student at School of Computer Science and Technique, Henan Polytechnic University, China.

His research interest is image processing.

Hong-Min Liu received the B. Sc. degree in electrical and information engineering from Xi’dian University, China in 2004, and the Ph.D. degree from Institute of Electronics, Chinese Academy of Sciences, China in 2009. Currently, she is an associate professor at School of Computer Science and Technique, Henan Polytechnic University, China.

Her research interest is image processing, especially on feature detection and matching.

ORCID iD: 0000-0001-9834-4087

Zhan-Qiang Huo received the B. Sc. degree in mathematics and applied mathematics from the Hebei Normal University of Science and Technology, China in 2003. He received the M. Sc. degree in computer software and theory, and the Ph.D. degree in circuit and system from Yanshan University, China in 2006 and 2009, respectively. Currently, he is an associate professor in the College of Computer Science and Technology at Henan Polytechnic University, China. He has published about 20 refereed journal and conference papers.

His research interests include computer software and theory, queuing systems and digital image processing.

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Wang, ZH., Song, QF., Liu, HM. et al. Absence importance and its application to feature detection and matching. Int. J. Autom. Comput. 13, 480–490 (2016). https://doi.org/10.1007/s11633-015-0925-7

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  • DOI: https://doi.org/10.1007/s11633-015-0925-7

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