Reconstruction and segmentation from sparse sequential X-ray measurements of wood logs

TL;DR

This paper introduces a novel approach combining a Dimension reduced Kalman Filter and unsupervised clustering to improve reconstruction and segmentation of wood logs from sparse sequential X-ray measurements, aiding industrial sawing.

Contribution

It proposes a new method integrating Kalman filtering and density-based clustering for accurate reconstruction and segmentation from undersampled sequential X-ray data.

Findings

Improved reconstruction quality with Kalman Filter.

Effective segmentation of internal wood log structures.

Robust detection of density anomalies in experimental data.

Abstract

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.