Optimal 3D object reconstruction and classification by separable moments via the Firefly algorithm

The 3D Hahn-Hahn-Charlier moments (HHCMs) are a novel collection of 3D separable Discrete Orthogonal Moments (DOM) that we introduce in this paper for the purpose of using them in classification and reconstruction applications. It should be noted that HHCM moments are characterized by parameters $\varphi_{1}, \gamma_{1}, \varphi_{2}, \gamma_{2}, a_{1}$ However, it was very important to optimize these parameters in order to obtain good results in classification and reconstruction. In this context, this paper adopts an approach to optimize the parameters $\varphi_{1}, \gamma_{1}, \varphi_{2}, \gamma_{2}, a_{1}$ of HHCMs based on the Firefly (FA) algorithm. The simulation results suggest that the proposed HHCM moments, based on the FA algorithm, provide a high level of quality in the reconstruction and classification of objects. Moreover, the comparison with other algorithms clearly demonstrates the superiority of the studied method.