Image recognition using new set of separable three-dimensional discrete orthogonal moment invariants

• Published in: Multimedia tools and applications Vol. 79; no. 19-20; pp. 13217 - 13245
• Main Authors: Batioua, Imad, Benouini, Rachid, Zenkouar, Khalid
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.05.2020; Springer Nature B.V
• Subjects: Basis functions; Computer Communication Networks; Computer Science; Computer vision; Data Structures and Information Theory; Experiments; Feature extraction; Invariants; Mathematical analysis; Methods; Multimedia; Multimedia Information Systems; Numerical stability; Object recognition; Pattern recognition; Polynomials; Special Purpose and Application-Based Systems; Invariant moment; 3D objects recognition; Separable discrete orthogonal moment
• ISSN: 1380-7501; 1573-7721
• DOI: 10.1007/s11042-019-08083-1

In this paper, we propose new sets of 3D separable discrete orthogonal moment invariants, named Racah-Tchebichef-Krawtchouk Moment Invariants (RTKMI), Racah-Krawtchouk-Krawtchouk Moment Invariants (RKKMI) and Racah-Racah-Kr-awtchouk Moment Invariants (RRKMI), for 3D image recognition. The basis functions of these new sets of moment invariants are represented by multivariate discrete orthogonal polynomials. We also present theoretical framework to derive their Rotation, Scaling and Translation (RST) invariants based on the 3D geometric moment invariants. Accordingly, the performance of these proposed separable moment invariants is evaluated under heterogeneous databases and through several appropriate experiments, including 3D image invariance against geometric deformations, local feature extraction, computation time and recognition accuracy, in comparison with the traditional moment invariants. The obtained results showed that our proposed separable moment invariant are very efficient in terms of object recognition, numerical stability and local feature extraction, and can be highly useful for computer vision applications.