Even if lots of object invariant descriptors have been proposed in the literature, putting them into practice in order to obtain a robust system face to several perturbations is still a studied problem. Comparative studies between the most commonly used descriptors put into obviousness the invariance of Zernike moments for simple geometric transformations and their ability to discriminate objec...

This paper proposes a robust and effective shape feature, which is based on a set of orthogonal complex moments of images known as Zernike moments. Zernike moment phase is usually not used in image description since it’s sensitive to image rotations. However, phase captures important image information, which is revealed by our numerical analysis of image reconstruction. We therefore propose com...

High-order Hu moment invariant functions have always been required to solve a variety of problems. Up to this point, there was no generalized approach to extend the first six Hu moment invariants to higher orders. Therefore, this paper presents a generalized algorithm to determine rotationally invariant Hu moment invariants of any desired order, which are invariant to scaling, translation and r...

On the basis of multi-sensor fusion algorithm, a target recognition algorithm based on Back Propagation (BP) neural networks and invariant moments was proposed. Invariant moment takes advantage of overall information of the targets. It has good differentiating effect and high identification technique. On the other hand, BP neural networks not only have the adaptive learning ability, but also ar...

This paper introduces a new efficient way for computing affine invariant features from gray-scale images. The method is based on a novel image transform which produces infinitely many different invariants, and is applicable directly to isolated image patches without further segmentation. Among methods in this class only the affine invariant moments have as low complexity as our method, but as k...

We suggest a method of constructing gauge invariant quark and gluon distributions that describe an abstract QCD observable and apply this method to analyze angular momentum of a hadron. In addition to the known quark and gluon polarized structure functions, we obtain gauge invariant distributions for quark and gluon orbital angular momenta, and consider some basic properties of these distributi...

Distributions of particles being transported through a nonlinear Hamiltonian system are studied. Using normal form techniques, a procedure to obtain invariant functions of moments of the distribution is given. These functions are invariant for the given Hamiltonian system and are called dynamic moment invariants. These techniques are used to obtain dynamic moment invariants for the nonlinear pe...

Zernike moments are widely used in several pattern recognition applications, as invariant descriptors of the image shape. Zernike moments have proved to be superior than other moment functions in terms of their feature representation capabilities. The major drawback with Zernike moments is the computational complexity. This paper presents a fast algorithm for the computation of Zernike moments ...

In this paper, a neural network (NN) based approach for translation, scale, and rotation invariant recognition of objects is presented. The utilized network is a Multi-Layer Perceptron (MLP) classifier with one hidden layer. The back-propagation learning is used for its training. The image is represented by rotation invariant features which are the magnitudes of the Zernike moments of the image...

We suggest a method of constructing gauge invariant quark and gluon distributions that describe an abstract QCD observable and apply this method to analyze angular momentum of a hadron. In addition to the known quark and gluon polarized structure functions, we obtain gauge invariant distributions for quark and gluon orbital angular momenta, and consider some basic properties of these distributi...