We have developed high density image processing techniques for finding the surface strain of an unprepared 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 artefacts. 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 three studies: mild steel under axial tension, the formation of kink bands in compressed carbon-fibre composite, and non-homogeneous strain fields in a welded aluminium alloy. Because the algorithm offers increased accuracy near motion contrast boundaries, its application has resulted in new mesomechanical observations
