Optimal Fundamental Matrix Computation : Algorithm and Reliability Analysis

This paper presents an optimal linear algorithm for computing the fundamental matrix from corresponding points over two images under an assumed model, which admits independent Gaussian noise that is not necessarily isotropic or homogeneous. We derive a theoretical bound and demonstrate by experiments that our algorithm delivers results in the vicinity of the bound. Simulated and real-image examples are shown to observe the performance of our algorithm and illustrate the reliability evaluation process.