An improved background segmentation algorithm for fringe projection profilometry based on Otsu method

In fringe projection profilometry, there always exist background and shadow areas in the captured fringe images. To improve the data quality, these areas should be recognized and deleted before the point cloud is reconstructed. For the existing algorithms, it is a nontrivial task to obtain a reasonable threshold to recognize the background and shadow areas from the fringe images. An inappropriate threshold usually leads to a misclassification of the object and background/shadow areas. In this paper, an improved adaptive thresholding method is built to address this problem. Instead of the gray level histogram, the modulation level histogram is utilized to compute the threshold. The modulation image is computed from all the captured images in the projection sequence. A novel objective function with a Gaussian weighted factor is proposed to improve Otsu's method. With this weighted factor, the between-class variance between the object and background/shadow is intensified. The new built objective function also makes the objective function plot much smoother, which makes the proposed method more suitable for noise image segmentation. Additionally, the weighted factor changes adaptively with the input images. Experiment results illustrate that the proposed scheme performs better than existing methods. The scan for an area on a car body rear demonstrates the effectiveness and feasibility of the proposed method in structured light measurement of real metal components.