Invariant image recognition by Zernike moments

Khotanzad, A.; Hong, Yaw Hua

doi:10.1109/34.55109

Cited by 1,687 publications

(857 citation statements)

References 14 publications

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“…The generic R-signature has been compared with angular radial transform (ART) [1], generic Fourier descriptor (GFD) [20], Zernike moments [9], R2DFM descriptor [17], and RMF descriptor [6]. Among the selected descriptors for comparison, R2DFM and RFM descriptors are also defined on the Radon transform.…”

Section: Resultsmentioning

confidence: 99%

The generalization of the R-transform for invariant pattern representation

Hoang

Tabbone

2012

Pattern Recognition

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Section: Resultsmentioning

confidence: 99%

The generalization of the R-transform for invariant pattern representation

Hoang

Tabbone

2012

Pattern Recognition

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“…The system detects skilled forgery with an accuracy of 71%. Khotanzad and Hong [7] studied the superiority of Zernike moment feature over regular moments and moments invariants. Sohail and Rashid [8] combined circularity with other structural features and have gained good results.…”

Section: Literature Reviewmentioning

confidence: 99%

“…They are simple. Translational and scale invariant shape descriptors [19]. Moment descriptors has the high quality in representing shapes [20].…”

Section: Feature Extractionmentioning

confidence: 99%

Offline Signature Verification System for Bank Cheques Using Zernike Moments, Circularity Property and Neural Network

D¹

2016

IJAIA

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“…Many normalization methods have been proposed to obtain di erent forms of the moment invariants [53][54][55]. It has been demonstrated by Teh and Chin [101] that Zernike moments are the best compared to the other four di erent moments (Legndre, Complex, Rational, and regular).…”

Section: Moment Invariantsmentioning

confidence: 99%

Brief review of invariant texture analysis methods

Zhang¹,

Tan

2002

Pattern Recognition

384

171

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This paper considers invariant texture analysis. Texture analysis approaches whose performances are not a ected by translation, rotation, a ne, and perspective transform are addressed. Existing invariant texture analysis algorithms are carefully studied and classiÿed into three categories: statistical methods, model based methods, and structural methods. The importance of invariant texture analysis is presented ÿrst. Each approach is reviewed according to its classiÿcation, and its merits and drawbacks are outlined. The focus of possible future work is also suggested. ?

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Invariant image recognition by Zernike moments

Cited by 1,687 publications

References 14 publications

The generalization of the R-transform for invariant pattern representation

The generalization of the R-transform for invariant pattern representation

Offline Signature Verification System for Bank Cheques Using Zernike Moments, Circularity Property and Neural Network

Brief review of invariant texture analysis methods

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