We consider geometry parameter estimation in industrial sawmill fan-beam X-ray tomography. In such industrial settings, scanners do not always allow identification of the location of the source-detector pair, which creates the issue of unknown geometry. This work considers two approaches for geometry estimation. Our first approach is a calibration object correlation method in which we calculate the maximum cross-correlation between a known-sized calibration object image and its filtered backprojection reconstruction and use differential evolution as an optimiser. The second approach is projection trajectory simulation, where we use a set of known intersection points and a sequential Monte Carlo method for estimating the posterior density of the parameters. We show numerically that a large set of parameters can be used for artefact-free reconstruction. We deploy Bayesian inversion with Cauchy priors for synthetic and real sawmill data for detection of knots with a very low number of measurements and uncertain measurement geometry.
翻译:我们考虑在工业锯木机扇光X射线摄影中进行几何参数估计。 在这种工业环境中,扫描仪并不总是能够辨别源-检测对对的位置,这就产生了未知几何学问题。 这项工作考虑了两种几何估计方法。 我们的第一种方法是校准物体相关方法,我们在这个方法中计算已知大小校准物体图像与其过滤后回射重建之间的最大交叉关系,并将差异演化作为优化器。 第二种方法是预测轨迹模拟,我们用一组已知交叉点和连续的蒙特卡洛方法来估计参数的外表密度。 我们用数字显示,大量参数可用于无艺术重建。 我们用贝斯恩斯前科和卡索西前科用于合成和真实锯木数据的转换,以非常低的测量数量和不确定的测量几何方法探测结。