For a fringe projection profilometry, the projector calibration is the key step to ensure the measurement accuracy of the system. However, conventional methods for the projector calibration are usually involved a complex system model and a calibration process. Therefore, an approach for accurate projector calibration based on the Grey Wolf Optimization (GWO) algorithm and the multi-layer perceptron (MLP) is proposed to simplify the process and improve the generalization. In this method, in order to compensate the errors of projected image coordinates of the calibrated corners obtained by using the projective transformation matrix, the parameters of the MLP network are optimized by using the GWO to construct the GWO-MLP network. Then, it is trained to compensate the errors of the coordinates of characteristic corners and is available to calibrate the projector. The experiments verify the feasibility and effectiveness of the proposed method.
Aiming at the fundamental role of railway wagon number recognition in railway freight management and railway wagon inspection, a railway wagon number recognition method based on Combined Grey Wolf Optimizer Support Vector Machine (C-GWO-SVM) is proposed. Aiming at the confusing numbers and letters in the railway wagon number dataset, the algorithm in this article first uses a set of GWO-SVM to perform multi-classification processing on the railway wagon number characters, and divides the numbers and letters in the dataset into easily distinguishable number and letter characters, and easily confusing number and letter characters, with a classification accuracy of 98.875%; Then two sets of GWO-SVM are used to classify and recognize the numbers and letters in the railway wagon number characters, with classification accuracy of 99.70% and 99.99%, respectively. The experimental results show that compared with Sparrow Search Algorithm (SSA) to optimize SVM multi-classification algorithm, the GWO-SVM algorithm has shorter parameter optimization time, higher recognition accuracy and faster recognition speed in the application of railway wagon number and letter characters recognition.
The phase unwrapping method is the important step of phase retrieval in fringe projection profilometry. Although the mask cut (MC) algorithm has been successfully applied in multiple fields, it also has inherent flaws. In order to overcome the shortcomings of MC algorithm, and synthesize the advantages of MC and quality-guided (QG) algorithm, a quality-guided mask-cutting (QG - MC) algorithm for phase unwrapping is proposed. The basic idea of QG - MC algorithm is to reduce the effects of noise on phase unwrapping at first., Then, the process of phase unwrapping is guided by the phase quality map from the point with the highest quality value to the point around that point. Take the point with the highest quality as the seed point, put its adjacent points into the queue, sort by quality value, and the new highest quality point is used as a seed point. Repeats the process until the queue is empty and the unwrapped phase will be obtained. To verify the feasibility and reliability of QG - MC algorithm, computer simulations and real experiments are carried out. The results show that the algorithm improves the efficiency of phase unwrapping.
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