Fundamental Matrix Estimation using Evolutionary Algorithms with Multi-Objective Functions

In this paper, we present the use of two evolutionary algorithms to estimate fundamental matrices. We first propose a modification of the Hybrid Taguchi Genetic Algorithm (HTGA) that employs a single objective function, either geometric or algebraic distance, for optimization. We then propose to use a multi-objective optimization algorithm, Intelligent Multi-Objective Evolutionary Algorithm (IMOEA), to optimize both geometric and algebraic distances concurrently. Our experiments show that the proposed modified HTGA (MHTGA) and IMOEA produce more accurate estimation of fundamental matrices than the traditional Genetic Algorithm (GA) and the original HTGA do.