Accurate calculation of Zernike moments

Singh, Chandan; Walia, Ekta; Upneja, Rahul

doi:10.1016/j.ins.2013.01.012

Cited by 63 publications

(16 citation statements)

References 32 publications

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“…To compute the Zernike moments of a PSSM matrix [67,68,69,70], the center of the matrix is taken as the origin and coordinates are mapped into a unit circle, i.e.,

x^{2} + y^{2} \leq 1

. Those values of matrix falling outside the unit disk are not used in the computation.…”

Section: Materials and Methodologymentioning

confidence: 99%

PCVMZM: Using the Probabilistic Classification Vector Machines Model Combined with a Zernike Moments Descriptor to Predict Protein–Protein Interactions from Protein Sequences

Wang

You

Xiao

et al. 2017

IJMS

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Protein–protein interactions (PPIs) are essential for most living organisms’ process. Thus, detecting PPIs is extremely important to understand the molecular mechanisms of biological systems. Although many PPIs data have been generated by high-throughput technologies for a variety of organisms, the whole interatom is still far from complete. In addition, the high-throughput technologies for detecting PPIs has some unavoidable defects, including time consumption, high cost, and high error rate. In recent years, with the development of machine learning, computational methods have been broadly used to predict PPIs, and can achieve good prediction rate. In this paper, we present here PCVMZM, a computational method based on a Probabilistic Classification Vector Machines (PCVM) model and Zernike moments (ZM) descriptor for predicting the PPIs from protein amino acids sequences. Specifically, a Zernike moments (ZM) descriptor is used to extract protein evolutionary information from Position-Specific Scoring Matrix (PSSM) generated by Position-Specific Iterated Basic Local Alignment Search Tool (PSI-BLAST). Then, PCVM classifier is used to infer the interactions among protein. When performed on PPIs datasets of Yeast and H. Pylori, the proposed method can achieve the average prediction accuracy of 94.48% and 91.25%, respectively. In order to further evaluate the performance of the proposed method, the state-of-the-art support vector machines (SVM) classifier is used and compares with the PCVM model. Experimental results on the Yeast dataset show that the performance of PCVM classifier is better than that of SVM classifier. The experimental results indicate that our proposed method is robust, powerful and feasible, which can be used as a helpful tool for proteomics research.

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“…To compute the Zernike moments of a PSSM matrix [67,68,69,70], the center of the matrix is taken as the origin and coordinates are mapped into a unit circle, i.e.,

x^{2} + y^{2} \leq 1

. Those values of matrix falling outside the unit disk are not used in the computation.…”

Section: Materials and Methodologymentioning

confidence: 99%

PCVMZM: Using the Probabilistic Classification Vector Machines Model Combined with a Zernike Moments Descriptor to Predict Protein–Protein Interactions from Protein Sequences

Wang

You

Xiao

et al. 2017

IJMS

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“…A method applied in estimating this range is the image reconstruction error. [15][16][17] This reconstruction error is determined by comparing the original input image to its reconstructed version from a set of PZMs at a specified pseudo Zernike polynomial order. The mean square reconstruction error ε() between the original image and its reconstructed versions at increasing orders is used as a comparative measure, as described in Reference 7.…”

Section: Pseudo Zernike Momentsmentioning

confidence: 99%

Restoration of defaced serial numbers using lock-in infrared thermography (Part II)

Unobe

Lau

Kalivas

et al. 2019

J. Spectral Imaging

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This paper details continuing work on the development of a substantive non-destructive method to recover defaced serial numbers stamped or laser engraved into metallic objects based on lock-in infrared thermography. This method relies on the existence of a local zone of plastic strain created from stamping pressures in mechanically stamped pieces and a heat-affected zone in laser engraved samples, both extending to depths below the visible characters. The grain structure within these zones is dislocated due to the external forces applied. These deformed areas are exposed to the surface when the serial numbers are defaced. Infrared thermography utilises the change in thermal conductivity, based on local variation in the thermal gradient from thermal energy applied at the surface. Observation of the thermal gradient at the surface as it propagates through the object allows for identification of these deformed regions and subsequent recovery of the serial number. Principal component analysis is used to enhance the thermographic images. Pseudo Zernike moments and subsequently multiple similarity measures are utilised to identify the numbers based on a reference library of numbers. Several fusion rules are used to obtain a consensus across the similarity measures. This process is tested on several datasets including stamped numbers on a gun barrel, laser engraved numbers on a needle holder and the defaced vehicle identification number of a stolen motorcycle.

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“…The kernel function ( , ) nm hr  of radial orthogonal moments is constructed on a particular type of radial orthogonal polynomial and an angular Fourier complex componential factor. Radial orthogonal moments mainly include Zernike moments [27] [29], pseudo-Zernike moments [8], Orthogonal Fourier-Mellin moments [25], Chebyshev-Fourier moments [21], Jacobi-Fourier moments [46], Bessel-Fourier moments [38], radial harmonic-Fourier moments [24], exponent-Fourier moments [36], radial-shifted Legendre moments [39], and circularly semi-orthogonal moments [33]. In addition to their image reconstruction property, the remarkable advantage of radial orthogonal moments lies in their ability to achieve rotation invariance.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

“… Fast and accurate computation of orthogonal moments can be easily achieved using the recursive scheme and symmetry property of the orthogonal basis function [3] [19] [27];…”

Section: Accepted Manuscriptmentioning

confidence: 99%