Homography matrix is a matric representation of the projective relation between the space plane and its corresponding
image plane in computer vision. It is widely used in visual metrology, camera calibration, 3D reconstruction and etc.
Therefore, the accurate estimation of the homography matrix is significant. Here, the quantum-behaved particle swarm
optimization method, which is global convergent, is first introduced into the estimation of homography matrix. When suited
cost function is chosen, enough point correspondences can be utilized to search the optimal homography matrix, which can
make the estimation accurately. For the purpose of evaluating the proposed method, simulations and experiments are
conducted to confirm the feasibility and robustness. The points obtained from the deviated homography matrix are reprojected
to the image plane to evaluate the accuracy. To compare with the proposed method, the Levenberg-Marquardt
method, which is a typical iterative minimization method, is utilized to obtain the homography matrix. Simulations and
experimental results show that the proposed method is reasonable, accurate, and with an excellent robustness. When 10
correspondences and 20 particles are utilized, the root mean square error of the re-projected points can reach about 0.019 mm.
Furthermore, our proposed method is not related with the initialization and less correlated with the chosen cost function,
which is the deficiency of the common estimation methods.