3D radial invariant of dual Hahn moments

• Published in: Neural computing & applications Vol. 30; no. 7; pp. 2283 - 2294
• Main Authors: El Mallahi, Mostafa, Zouhri, Amal, Mesbah, Abderrahim, Qjidaa, Hassan
• Format: Journal Article
• Language: English
• Published: London Springer London 01.10.2018; Springer Nature B.V
• Subjects: Artificial Intelligence; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Data Mining and Knowledge Discovery; Image Processing and Computer Vision; Image reconstruction; Invariants; Object recognition; Original Article; Pattern recognition; Polynomials; Probability and Statistics in Computer Science; Representations; 2D and 3D rotation invariants; Radial dual Hahn moments; 3D image; Recognition; Classification
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-016-2782-x

In this work, we propose new sets of 2D and 3D rotation invariants based on orthogonal radial dual Hahn moments, which are orthogonal on a non-uniform lattice. We also present theoretical mathematics to derive them. Thus, this paper presents in the first case new 2D radial dual Hahn moments based on polar representation of an image by one-dimensional orthogonal discrete dual Hahn polynomials and a circular function. The dual Hahn polynomials are general case of Tchebichef and Krawtchouk polynomials. In the second case, we introduce new 3D radial dual Hahn moments employing a spherical representation of volumetric image by one-dimensional orthogonal discrete dual Hahn polynomials and a spherical function, which are orthogonal on a non-uniform lattice. The 2D and 3D rotational invariants are extracts from the proposed 2D and 3D radial dual Hahn moments respectively. In order to test the proposed approach, three problems namely image reconstruction, rotational invariance and pattern recognition are attempted using the proposed moments. The result of experiments shows that the radial dual Hahn moments have performed better than the radial Tchebichef and Krawtchouk moments, with and without noise. Simultaneously, the mentioned reconstruction converges quickly to the original image using 2D and 3D radial dual Hahn moments, and the test images are clearly recognized from a set of images that are available in COIL-20 database for 2D image and PSB database for 3D image.