Explicit invariance of Cartesian Zernike moments

Belkasim, Saeid; Hassan, Esraa; Obeidi, T.

doi:10.1016/j.patrec.2007.05.010

Cited by 19 publications

(19 citation statements)

References 18 publications

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“…Among the family of moment invariants, Zernike moments are one of the most commonly used feature extractor and have been used in variety of applications [14], [15], [16]. In the proposed technique, Zernike moments based shape features are used for ear verification.…”

Section: Zernike Momentsmentioning

confidence: 99%

Ear Localization from Side Face Images using Distance Transform and Template Matching

Prakash

Jayaraman

Gupta

2008

2008 First Workshops on Image Processing Theory, Tools and Applications

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Abstract-The paper presents an efficient distance transform and template based technique for automatic ear localization from a side face image. The technique first segments skin and non-skin regions in the face and then uses template based approach to find the ear location within the skin regions. Ear detection proceeds as follows. First, edge map of the skin regions is computed and further processed to eliminate the spurious edges based on length and curvature based criterion. After getting the clean edge map, its distance transform is obtained on which ear localization process is carried out. Distance transform image of the edge map of an off-line created ear template is employed for ear localization. A Zernike moment based shape descriptor is used to verify the detections. The technique is tested on IIT Kanpur ear database which contains around 150 ear images and found to be giving 95.2% accuracy.

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Section: Zernike Momentsmentioning

confidence: 99%

Ear Localization from Side Face Images using Distance Transform and Template Matching

Prakash

Jayaraman

Gupta

2008

2008 First Workshops on Image Processing Theory, Tools and Applications

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“…Moment invariants have been extensively studied and widely used in shape recognition and identification since they were first proposed by Hu [2]. Since then, many other kinds of moments have been proposed in the literature, including Zernike moments [3][4][5], Legendre moments [6,7], Fourier-Mellin moments [8][9][10], etc. [2].…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study on Weighted Central Moment and Its Application in 2D Shape Retrieval

et al. 2016

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Moment invariants have been extensively studied and widely used in object recognition. The pioneering investigation of moment invariants in pattern recognition was due to Hu, where a set of moment invariants for similarity transformation were developed using the theory of algebraic invariants. This paper details a comparative analysis on several modifications of the original Hu moment invariants which are used to describe and retrieve two-dimensional (2D) shapes with a single closed contour. The main contribution of this paper is that we propose several different weighting functions to calculate the central moment according to human visual processing. The comparative results are detailed through experimental analysis. The results suggest that the moment invariants improved by weighting functions can get a better retrieval performance than the original one does.

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“…Although the first method is computationally efficient [26][27], it is impractical since all images need to be resampled, which is difficult and tedious operation for large image databases. The second approach does not suffer from that kind of problem and therefore is more practical [1]. In addition, expressing Zernike moments in Cartesian coordinates enable them to be explicitly invariant to scale, translation and rotation [1].…”

Section: Introductionmentioning

confidence: 99%

“…The second approach does not suffer from that kind of problem and therefore is more practical [1]. In addition, expressing Zernike moments in Cartesian coordinates enable them to be explicitly invariant to scale, translation and rotation [1].…”

Section: Introductionmentioning

confidence: 99%

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Improving stability and invariance of Cartesian Zernike moments

Zhao

Belkasim

2012

2012 IEEE Southwest Symposium on Image Analysis and Interpretation

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Zernike moments are widely used in shape retrieval, recognition and classification. The rotational invariance property of Zernike moments is very simple to achieve, due to their separable magnitude-phase property. However, Zernike moments are not directly invariant to scale and translation. Recently Cartesian Zernike moments invariants (CZMI) were introduced to directly make Zernike moments invariant under translation and scale. Although CZMI reduced scale error considerably, they are inconsistent and scale error increases for high aspect ratio images. In this paper, we propose a new scale invariance parameter, which reduces scale errors, improves the stability of scale invariance and is more consistent and stable for processing all images including the ones with large aspect ratios.

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Explicit invariance of Cartesian Zernike moments

Cited by 19 publications

References 18 publications

Ear Localization from Side Face Images using Distance Transform and Template Matching

Ear Localization from Side Face Images using Distance Transform and Template Matching

A Comparative Study on Weighted Central Moment and Its Application in 2D Shape Retrieval

Improving stability and invariance of Cartesian Zernike moments

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