Invariant texture retrieval using modified Zernike moments

Sim, Donggyu; Kim, Hae-Kwang; Park, Rae-Hong

doi:10.1016/j.imavis.2003.11.003

Cited by 47 publications

(17 citation statements)

References 28 publications

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“…They are used, after making them invariant to scale and translation, as object descriptors in pattern recognition applications [3][4][5][17][18][19] and in image retrieval tasks [20,21] with considerable results. Recently, due to their popularity, many methods for accelerating their computation time have been proposed [9,[22][23][24][25][26] in order to make them more useful, especially in real time applications, where time is a critical factor.…”

Section: Zernike Momentsmentioning

confidence: 99%

Numerical error analysis in Zernike moments computation

Papakostas

Boutalis

Papaodysseus

et al. 2006

Image and Vision Computing

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Section: Zernike Momentsmentioning

confidence: 99%

Numerical error analysis in Zernike moments computation

Papakostas

Boutalis

Papaodysseus

et al. 2006

Image and Vision Computing

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“…They are used, after making them invariant to scale and translation, as object descriptors in pattern recognition applications [9][10][11][20][21][22][23]31,33] and in image retrieval tasks [14,27] with considerable results.…”

Section: Zernike Momentsmentioning

confidence: 99%

A new class of Zernike moments for computer vision applications

Papakostas

Boutalis

Karras

et al. 2007

Information Sciences

112

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“…Other important properties include robustness against transformational noise and excellent reconstruction capabilities. Owing to these properties, the ZMs were applied in the fields of character recognition [3], watermarking [4,5], image retrieval [6], texture retrieval [7], face recognition [8] and image reconstruction [9]. Pseudo-Zernike moments (PZMs) were given by Bhatia and Wolf [10].…”

Section: Introductionmentioning

confidence: 99%

Empirical Analysis of Rotation Invariance in Moment Coefficients

Ranade¹,

Anand²

2015

IJCA

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Moments can be viewed as powerful image descriptors that capture global characteristics of an image. The magnitude of the moment coefficients is said to be invariant under geometrical transformations like rotation which makes them suitable for most of the recognition applications. But in practice, the invariance of moment coefficients is compromised due to the errors in computation. This paper presents an empirical study of some popularly used moment functions to find out the robust coefficients under rotation. The selected robust coefficients are used in face recognition under in-plane rotation. Experimental results demonstrate that the performance of the proposed method comes at par with the performance of the traditional method by using lesser number of moment coefficients and thus results in significant saving in the feature extraction time.

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Invariant texture retrieval using modified Zernike moments

Cited by 47 publications

References 28 publications

Numerical error analysis in Zernike moments computation

Numerical error analysis in Zernike moments computation

A new class of Zernike moments for computer vision applications

Empirical Analysis of Rotation Invariance in Moment Coefficients

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