Some Researches On The Estimate Of Camera Intrinsic Parameters

In this paper, we mainly discuss the technology of estimating the camera intrinsic parameters based on multiple view geometry, which includes the algorithms and experiments of fundamental matrix, camera self-calibration, etc. The main work is as following:(1) Robust algorithms for fundamental matrix. It is the foundation of camera calibration. We firstly introduce several common linear algorithms of fundamental matrix, and then we give the algorithm named as six point Random Sample Consensus (RANSAC) algorithm.(2) Self-calibration based on the Singular Value Decomposition (SVD) of fundamental matrix. The proposed method relies on the Singular Value Decomposition of fundamental matrix, which leads to a particularly simple form of the Kruppa equations optimized by conjugate gradient method. The derivation doesn't need the somewhat non-intuitive geometric concept of the absolute conic. At last, we give the result of experiments.(3) Self-calibration under the constraints of motion parameters. It is usually equal to the self-calibration based on active vision. We first introduced a self-calibration algorithm based on the epipoles, Then, we give a new algorithm under the constraints of camera motion parameters, it can get the 5 intrinsic parameters of the camera linearly and uniquely, at last, we give the result of experiment, and verify the stability of 2-point algorithm for fundamental matrix.