Affordable Access

deepdyve-link
Publisher Website

New fractional-order shifted Gegenbauer moments for image analysis and recognition

Authors
  • Hosny, Khalid M.1
  • Darwish, Mohamed M.2
  • Eltoukhy, Mohamed Meselhy3, 4
  • 1 Information Technology Department, Faculty of Computers and Informatics, Zagazig University, Zagazig 44519, Egypt
  • 2 Mathematics Department, Faculty of Science, Assiut University, Assiut 71516, Egypt
  • 3 Computer Science Department, Faculty of Computers and Informatics, Suez Canal University, Ismailia, Egypt
  • 4 College of Computing and Information Technology, Khulais, University of Jeddah, Saudi Arabia
Type
Published Article
Journal
Journal of Advanced Research
Publisher
Elsevier
Publication Date
Jun 01, 2020
Volume
25
Pages
57–66
Identifiers
DOI: 10.1016/j.jare.2020.05.024
PMID: 32922974
PMCID: PMC7474242
Source
PubMed Central
Keywords
License
Unknown

Abstract

Orthogonal moments are used to represent digital images with minimum redundancy. Orthogonal moments with fractional-orders show better capabilities in digital image analysis than integer-order moments. In this work, the authors present new fractional-order shifted Gegenbauer polynomials. These new polynomials are used to define a novel set of orthogonal fractional-order shifted Gegenbauer moments (FrSGMs). The proposed method is applied in gray-scale image analysis and recognition. The invariances to rotation, scaling and translation (RST), are achieved using invariant fractional-order geometric moments. Experiments are conducted to evaluate the proposed FrSGMs and compare with the classical orthogonal integer-order Gegenbauer moments (GMs) and the existing orthogonal fractional-order moments. The new FrSGMs outperformed GMs and the existing orthogonal fractional-order moments in terms of image recognition and reconstruction, RST invariance, and robustness to noise.

Report this publication

Statistics

Seen <100 times