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ADVANCES IN THE IMAGE ANALYSIS BY MOMENT INVARIANTS

Ren Ai-zhen

Open publisher page 1 citations

Abstract

Invariant moments are highly concentrated image features that are shift-,rotation-,scale-and intensity-invariant.M.K.Hu first introduced seven moment invariants in 1961,based on methods of algebraic invariants.Later studies indicated that the orthogonal moments have the best overall performance in terms of noise sensitivity,information redundancy,and capability of image description.The ideal orthogonal moments are Zernike Moments,Orthogonal Fourier-Mellin Moments, Chebyshev-Fourier Moments,Pseudo-Jacobi(p=4,q=3)-Fourier Moments.Especially,many reports have been published about image analysis and pattern recognition with orthogonal moments in recent years.Therefore,the theory of invariant moments and their application to image analysis and pattern recognition have a good future.The concepts and the applications of the invariant moments were systematically introduced in this paper.

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What this paper is about

Invariant moments are highly concentrated image features that are shift-,rotation-,scale-and intensity-invariant.M.K.Hu first introduced seven moment invariants in 1961,based on methods of algebraic invariants.Later studies indicated that the orthogonal moments have the best overall performance in terms of noise sensitivity,information redundancy,and capability of image description.The ideal orthogonal moments are Zernike Moments,Orthogonal Fourier-Mellin Moments, Chebyshev-Fourier Moments,Pseudo-Jacobi(p=4,q=3)-Fourier Moments.Especially,many reports have been published about image analysis and pattern recognition with orthogonal moments in recent years.Therefore,the theory of invariant moments and their application to image analysis and pattern recognition have a good future.The concepts and the applications of the invariant moments were systematically introduced in this paper.

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Available abstract

Invariant moments are highly concentrated image features that are shift-,rotation-,scale-and intensity-invariant.M.K.Hu first introduced seven moment invariants in 1961,based on methods of algebraic invariants.Later studies indicated that the orthogonal moments have the best overall performance in terms of noise sensitivity,information redundancy,and capability of image description.The ideal orthogonal moments are Zernike Moments,Orthogonal Fourier-Mellin Moments, Chebyshev-Fourier Moments,Pseudo-Jacobi(p=4,q=3)-Fourier Moments.Especially,many reports have been published about image analysis and pattern recognition with orthogonal moments in recent years.Therefore,the theory of invariant moments and their application to image analysis and pattern recognition have a good future.The concepts and the applications of the invariant moments were systematically introduced in this paper.

Key concepts: Velocity Moments, Image moment, Zernike polynomials, Invariant (physics), Mathematics, Fourier transform, Moment (physics), Generalized method of moments

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ADVANCES IN THE IMAGE ANALYSIS BY MOMENT INVARIANTS — Research Paper | ScholarLens