Image analysis using new set of separable two-dimensional discrete orthogonal moments based on Racah polynomials

• Published in: EURASIP journal on image and video processing Vol. 2017; no. 1; pp. 1 - 17
• Main Authors: Batioua, Imad, Benouini, Rachid, Zenkouar, Khalid, Fadili, Hakim El
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 02.03.2017; Springer Nature B.V
• Subjects: Biometrics; Engineering; Feature extraction; Image analysis; Image classification; Image processing; Image Processing and Computer Vision; Image reconstruction; Invariants; Pattern Recognition; Polynomials; Signal,Image and Speech Processing; Translations; Separable discrete orthogonal moments; Moment invariants; Local feature extraction; Gray-level image reconstruction; Classical discrete orthogonal polynomials; Image classification
• ISSN: 1687-5281; 1687-5176; 1687-5281
• DOI: 10.1186/s13640-017-0172-7

In this paper, we propose three new separable two-dimensional discrete orthogonal moments baptized: RTM (Racah-Tchebichef moments), RKM (Racah-Krawtchouk moments), and RdHM (Racah-dual Hahn moments). We present a comparative study between our proposed separable two-dimensional discrete orthogonal moments and the classical ones, in terms of gray-level image reconstruction accuracy, including noisy and noise-free conditions. Furthermore, in this study, the local feature extraction capabilities of the proposed moments are described. Finally, a new set of RST (rotation, scaling, and translation) invariants, based on separable proposed moments, is introduced in this paper for the first time, and their description performances are highly tested as pattern features for image classification in comparison with the traditional moment invariants. The experimental results show that the new set of moments is potentially useful in the field of image analysis.