In industrial applications, it is common to scan objects on a moving conveyor belt. If slice-wise 2D computed tomography (CT) measurements of the moving object are obtained we call it a sequential scanning geometry. In this case, each slice on its own does not carry sufficient information to reconstruct a useful tomographic image. Thus, here we propose the use of a Dimension reduced Kalman Filter to accumulate information between slices and allow for sufficiently accurate reconstructions for further assessment of the object. Additionally, we propose to use an unsupervised clustering approach known as Density Peak Advanced, to perform a segmentation and spot density anomalies in the internal structure of the reconstructed objects. We evaluate the method in a proof of concept study for the application of wood log scanning for the industrial sawing process, where the goal is to spot anomalies within the wood log to allow for optimal sawing patterns. Reconstruction and segmentation quality are evaluated from experimental measurement data for various scenarios of severely undersampled X-measurements. Results show clearly that an improvement in reconstruction quality can be obtained by employing the Dimension reduced Kalman Filter allowing to robustly obtain the segmented logs.

翻译：在工业应用中，通常需要在移动传送带上扫描物体。若对运动物体进行逐层的二维计算机断层扫描（CT）测量，则称为序列扫描几何结构。在此情况下，单层切片本身无法提供足够信息以重建有效的断层图像。因此，本文提出采用降维卡尔曼滤波器来累积层间信息，从而实现足够精确的重建以用于后续物体评估。此外，我们采用一种名为密度峰值算法的无监督聚类方法，对重建物体内部结构进行分割并识别密度异常区域。以工业锯切过程中的木原木扫描为应用场景开展概念验证研究，该方法旨在识别木原木内部异常区域以实现最优锯切模式。基于实验测量数据，评估了多种严重欠采样X射线测量场景下的重建与分割质量。结果表明，采用降维卡尔曼滤波器可显著提升重建质量，从而稳健地获取分割后的原木图像。