An EA-Based Method for Estimating the Fundamental Matrix

The camera calibration problem consists in estimating intrinsic and extrinsic parameters. It can be solved by computing a 3x3 matrix enclosing such parameters - the fundamental matrix -, which can be obtained from a set of corresponding points. Nevertheless, in practice, corresponding points may be falsely matched or badly located, due to occlusion and ambiguity. Moreover, if the set of corresponding points does not include information on existing scene depth, the estimated fundamental matrix may not be able to correctly recover the epipolar geometry. In this paper, an EA-based method for accurately selecting estimated corresponding points is introduced. It considers geometric issues that were ignored in previous EA-based approaches. Two selection operators were evaluated and obtained similar results. Additionally, a mutation operator is designed to tackle bad located points by shifting disparity vectors. An inter-technique comparison is performed against a standard camera calibration method. The qualitative evaluation is conducted by analysing obtained epipolar lines, regarding expected appearance, based on a-priori knowledge of camera systems during the capturing process. The quantitative evaluation of the proposed method is based on residuals. Experimental results shown the proposed method is able to correctly reconstruct the epipolar geometry.