Neural computation of the fundamental matrix

The fundamental matrix combines the mutual relation of the corresponding points in the two images of an observed scene. This relation, known also as an epipolar geometry, allows for a further depth reconstruction, image rectification or camera calibration. Thus, computation of the fundamental matrix has been one of the most important problems of computer vision. Many linear and non-linear methods were already proposed to solve this problem. However, due to the nature of image processing there is no unique solution and each method exhibits some exclusive properties. In this paper a neural approach to the computation of the fundamental matrix is proposed. For this purpose, the special configuration of the back-propagation neural network was developed. Both, linear and non-linear versions are also discussed.