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Combined invariants to similarity transformation and to blur using orthogonal Zernike moments.

Abstract : The derivation of moment invariants has been extensively investigated in the past decades. In this paper, we construct a set of invariants derived from Zernike moments which is simultaneously invariant to similarity transformation and to convolution with circularly symmetric point spread function (PSF). Two main contributions are provided: the theoretical framework for deriving the Zernike moments of a blurred image and the way to construct the combined geometric-blur invariants. The performance of the proposed descriptors is evaluated with various PSFs and similarity transformations. The comparison of the proposed method with the existing ones is also provided in terms of pattern recognition accuracy, template matching and robustness to noise. Experimental results show that the proposed descriptors perform on the overall better.
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https://www.hal.inserm.fr/inserm-00522932
Contributor : Lotfi Senhadji <>
Submitted on : Sunday, October 3, 2010 - 8:58:18 PM
Last modification on : Wednesday, June 24, 2020 - 4:18:44 PM
Long-term archiving on: : Tuesday, January 4, 2011 - 2:29:30 AM

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Chen Beijing, Huazhong Shu, Hui Zhang, Gouenou Coatrieux, Limin Luo, et al.. Combined invariants to similarity transformation and to blur using orthogonal Zernike moments.. IEEE Transactions on Image Processing, Institute of Electrical and Electronics Engineers, 2011, 20 (2), pp.345-60. ⟨10.1109/TIP.2010.2062195⟩. ⟨inserm-00522932⟩

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