Generalized dual Hahn moment invariants

Authors:
E. G. Karakasis;G. A. Papakostas;D. E. Koulouriotis;V. D. Tourassis
Affiliations:
Department of Production Engineering and Management, Democritus University of Thrace, 67100 Xanthi, Greece;Department of Industrial Informatics, TEI of Kavala, Human-Machines Interaction (HMI) Laboratory, 65404 Kavala, Greece;Department of Production Engineering and Management, Democritus University of Thrace, 67100 Xanthi, Greece;Department of Production Engineering and Management, Democritus University of Thrace, 67100 Xanthi, Greece
Venue:
Pattern Recognition
Year:
2013

Citing 15
Cited 0

One-Shot Learning of Object Categories

IEEE Transactions on Pattern Analysis and Machine Intelligence
Image analysis by discrete orthogonal Racah moments

Signal Processing
A new class of Zernike moments for computer vision applications

Information Sciences: an International Journal
Robust Object Recognition with Cortex-Like Mechanisms

IEEE Transactions on Pattern Analysis and Machine Intelligence
Image analysis by discrete orthogonal dual Hahn moments

Pattern Recognition Letters
Image Analysis Using Hahn Moments

IEEE Transactions on Pattern Analysis and Machine Intelligence
Invariant optimal feature selection: A distance discriminant and feature ranking based solution

Pattern Recognition
Novel moment invariants for improved classification performance in computer vision applications

Pattern Recognition
Reconstruction of tomographic images from limited range projections using discrete Radon transform and Tchebichef moments

Pattern Recognition
Novel maximum-margin training algorithms for supervised neural networks

IEEE Transactions on Neural Networks
Image quality assessment by discrete orthogonal moments

Pattern Recognition
Affine moment invariants generated by graph method

Pattern Recognition
Image analysis by Tchebichef moments

IEEE Transactions on Image Processing
Image analysis by Krawtchouk moments

IEEE Transactions on Image Processing
Enhanced Biologically Inspired Model for Object Recognition

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

Quantified Score

Hi-index	0.01

Visualization

Abstract

In this work we introduce a generalized expression of the weighted dual Hahn moment invariants up to any order and for any value of their parameters. In order for the proposed invariants to be formed, the weighted dual Hahn moments (up to any order and for any value of their parameters) are expressed as a linear combination of geometric ones. For this reason a formula expressing the nth degree dual Hahn polynomial, for any value of its parameters, as a linear combination of monomials (c"r.x^r), is proved. In addition, a recurrent relation for the fast computation of the aforementioned monomials coefficients (c"r) is also given. Moreover, normalization aspects of the generalized weighted dual Hahn moment invariants are discussed, while a modification of them is proposed in order to avoid their numerical instabilities. Finally, experimental results and classification scenarios, including datasets of natural scenes, evaluate the proposed methodology.