Fundamental matrix estimation by multiobjective genetic algorithm with Taguchi's method
We propose a multiobjective genetic algorithm to compute the fundamental matrix, which are the foundation of multiview geometry and calibration in many 3D applications such as 3D reconstruction. The proposed method is a modification of the Intelligent Multiobjective Evolutionary Algorithm (IMOEA) [7] coupled with Taguchi's method [14]. Our design focuses are the fitness assignment of multiple objective functions, the diversity preservation, and the addition of an elite set. Moreover, we propose to include an additional random population besides the original initial population in genetic algorithms. In each generation we replace the random population and select only the non-dominated individuals into the elite set. The proposed method can explore more general solution space and can locate better solutions. We validate the proposed methods by demonstrating the effectiveness of the proposed methods to estimate of the fundamental matrices.
fundamental matrix multiobjective genetic algorithms taguchi's method 3d reconstruction random population