Robust Deformation Estimation in Wood-Composite Materials using Variational Optical Flow

TL;DR

This paper presents an automated computer vision method that estimates humidity-induced deformations in wood-composite materials by computing dense optical flow fields from scans, enabling precise quantification of deformation coefficients.

Contribution

It introduces a novel variational optical flow approach with customizable regularization for automatic deformation analysis in wood composites from scan data.

Findings

Automated registration and optical flow computation effectively capture deformation fields.

The method accurately calculates deformation coefficients and their variance.

Regularization suppresses artifacts from surface changes like mold and cracks.

Abstract

Wood-composite materials are widely used today as they homogenize humidity related directional deformations. Quantification of these deformations as coefficients is important for construction and engineering and topic of current research but still a manual process. This work introduces a novel computer vision approach that automatically extracts these properties directly from scans of the wooden specimens, taken at different humidity levels during the long lasting humidity conditioning process. These scans are used to compute a humidity dependent deformation field for each pixel, from which the desired coefficients can easily be calculated. The overall method includes automated registration of the wooden blocks, numerical optimization to compute a variational optical flow field which is further used to calculate dense strain fields and finally the engineering coefficients and their variance throughout the wooden blocks. The methods regularization is fully parameterizable which allows to model and suppress artifacts due to surface appearance changes of the specimens from mold, cracks, etc. that typically arise in the conditioning process.