In this article, we will present a new set of hybrid polynomials and their corresponding
moments, with a view to using them for the localization, compression and reconstruction of …

Krawtchouk polynomials (KPs) are discrete orthogonal polynomials associated with the
Gauss hypergeometric functions. These polynomials and their generated moments in 1D or …

In this paper, we will present a new method of fast and stable computation of 3D image
reconstruction using a new collection of 3D separable discrete orthogonal moments called …

In this paper, we will present a new method of partial reconstruction of the 3D image. The
latter is based on the following two concepts: one is the stable discrete orthogonal Hahn …

In this paper, we will present a new set of 2D and 3D continuous orthogonal moments based
on generalized Laguerre orthogonal polynomials (GLPs) for 2D and 3D image analysis …

The identification of objects in binary images is a fundamental task in image analysis and
pattern recognition tasks. The Euler number of a binary image is an important topological …

Herein, a parallel implementation of Discrete Orthogonal moments on block represented
images is investigated. Moments and moment functions have been used widely as features …

Pattern descriptors invariant to rotation, scaling, and translation represents an important
direction in the elaboration of the real time object recognition systems. In this article, the new …

Krawtchouk polynomials (KPs) are discrete orthogonal polynomials associated with the
Gauss hypergeometric functions. These polynomials and their generated moments in 1D or …

In the article the analysis of different approaches to invariant target recognition was made,
such as based on the support vector machines, deep learning techniques, neural networks …