  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.
