Pseudo-Zernike moment invariants to blur degradation and their use in image recognition

Authors:
Xiubin Dai;Tianliang Liu;Huazhong Shu;Limin Luo
Affiliations:
School of Geography and Biological Information, Nanjing University of Posts and Telecommunications, Nanjing, China;College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing, China;Laboratory of Image Science and Technology, School of Computer Science and Engineering, Southeast University, Nanjing, China;Laboratory of Image Science and Technology, School of Computer Science and Engineering, Southeast University, Nanjing, China
Venue:
IScIDE'12 Proceedings of the third Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
Year:
2012

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Abstract

The acquired images often provide a degraded version of the true scene due to the imperfect imaging devices or imaging conditions. Therefore recognition of blurred images has become a key task in pattern recognition and moment invariant-based methods play an important role in this field. In this paper, we construct a new set of invariants using Pseudo-Zernike moments which are invariant to convolution with circularly symmetric point spread function (PSF). The experimental results show that proposed invariants have better performance in terms of invariance and robustness to noise with the comparison to the blur invariants derived from Zernike moments whatever the PSF and noise.