A novel efficient camera calibration approach based on K-SVD sparse dictionary learning

Abstract

Camera calibration is essential for accurate product visual inspection. In this paper, a novel efficient camera calibration approach based on K-Singular Value Decomposition (K-SVD) sparse dictionary learning is presented, in which, (1) the nonlinear optimization model with ten calibration parameters is formulated, (2) a large amount of images of checkerboards are acquired offline at different locations in the working volume of the camera to construct a sparse dictionary through K-SVD sparse dictionary learning, which enables such non-linear optimization model appropriately initialized and efficiently converges, and (3) in addition, the sparse dictionary can be further updated with the new acquired image so that it can be used to timely characterize the camera variation due to lens aging, and still be used to obtain appropriate initial values. Finally, three experiments are demonstrated to validate the efficiency of the presented approach. The results show that (1) the camera can be accurately calibrated with only one image, which can save almost more than 60 percent calibration time, and is very beneficial to online calibration for quality inspection, and (2) the calibration is much more accurate than that of traditional approaches with only one image having identical grid pixels.