Abstract It is one of the key problems in machine vision measurement to estimate the center position of noisy 1-dimensional images (grayscale curves) lacking shape prior information. With matching error between the curve and its mirror image being used as the symmetry evaluation function, a symmetry center fitting algorithm is proposed. And the and the least square method is used to calculate the best matching point as the symmetry center. The algorithm needs iterative calculation, and may converge to wrong position. By analyzing the possible local convergence caused by the initial value selection of iterative, it is proved that the local extreme point may only appear within the half pixels adjacent to the true value, a convergence point verification strategy is proposed, which solves the problem of erroneous convergence. The robustness of the algorithm under various disturbances is confirmed through simulation and real image verification. Under the interference of strong noise, the position variation of gray centroid method can reach tens or even hundreds of pixels, while the root-mean-square value of the position variation of symmetric centroid fitting algorithm can still remain at about 1 pixel. Under other noise conditions, the performance of symmetric centroid fitting algorithm is also far superior to gray centroid method.
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2024, Vol. 45 
