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We present an image analysis technique for examining the fine scale (Kolmogorov scale) variations of the mixing process in a turbulent flow. The objective is to trace the interaction of two flows by identifying their motions in a sequence of 3-D images obtained with a system based on a high-speed solid state camera. After briefly describing the imaging process and the particularities related to the capture of quasi-continuous 3-D image sequences, we focus on theoretical and implementational issues associated with feature tracking in 3-D image sequences. We present the extension of least squares matching from pixels, associated with 2- D images, to voxels, associated with 3-D images. The use of additional constraints of radiometric and/or geometric nature strengthens the matching solution. In addition, the large amount of data associated with 3-D image sequences in general, and the high and multidirectional velocities involved in this application in particular, make the division of an efficient matching strategy quite important.
Maas et al. (Wed,) studied this question.