Classification and Recognition of 3D Image of Charlier moments using a Multilayer Perceptron Architecture

In this paper, we propose a new method for classification and Recognition of 3D Image Charlier moments using a Multilayer Perceptron architecture. The Multilayer Perceptron is an artificial neural network to evaluate the efficient structure in the non-linear systems. However, the determination of its architecture and weights is a fundamental issue due to their direct impact on the network convergence and performance. The robustness of the proposed approach have provided under many transforms. The experimental results show that our approaches are more robust than 3D Tchebichef moments.