Image processing issues in digital strain mapping

We have developed high density image processing techniques for finding the surface strain of an untreated sample of material from a sequence of images taken during the application of force from a test rig. Not all motion detection algorithms have suitable functional characteristics for this task, as image sequences are characterised by both short- and long-range displacements, non-rigid deformations, as well as a low signal-to-noise ratio and methodological artifacts. We show how a probability-based motion detection algorithm can be used as a high confidence estimator of the strain tensor characterising the deformation of the material. An important issue discussed is how to minimise the number of image brightness differences that need to be calculated. We give results from two studies of materials under axial tension: a sample of aluminium alloy exhibiting a propagating plastic deformation, and a preparation of deer antler bone, a natural composite material.