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Fluid motion estimation from time-sequenced images is a significant image analysis task. Its application is widespread in experimental fluidics research and many related areas like biomedical engineering and atmospheric sciences. In this paper, we present a novel flow computation framework to estimate the flow velocity vectors from two consecutive image frames. In an energy minimization-based flow computation, we propose a novel data fidelity term, which: 1) can accommodate various measures, such as cross-correlation or sum of absolute or squared differences of pixel intensities between image patches; 2) has a global mechanism to control the adverse effect of outliers arising out of motion discontinuities, proximity of image borders; and 3) can go hand-in-hand with various spatial smoothness terms. Further, the proposed data term and related regularization schemes are both applicable to dense and sparse flow vector estimations. We validate these claims by numerical experiments on benchmark flow data sets. [ABSTRACT FROM AUTHOR]

We propose a temporal modeling approach for determining image motion from a sequence of images wherein the inherent motion is periodic over time. To exploit the periodic nature of the motion, we use a Fourier harmonic representation model the temporal evolution of the motion field for the entire sequence. We then determine the motion field simultaneously for the different image frames by estimating the parameters of this representation model, where the model order in the Fourier representation serves as a regularization parameter on the temporal coherence of the motion field. This approach can take advantage of the statistics of all the available data in the image sequence. In our experiments, we tested the proposed approach on several motion types at different noise levels, including translational motion, convergent/ divergent motion, and cardiac motion. Our results demonstrate that this approach could lead to more robust estimation the motion field in the presence of strong imaging noise compared to a frame-by-frame estimation approach. [ABSTRACT FROM AUTHOR]

Optical flow can be reliably estimated between areas visible in two images, but not in occlusion areas. If optical flow is needed in the whole image domain, one approach is to use additional views of the same scene. If such views are unavailable, an often-used alternative is to extrapolate optical flow in occlusion areas. Since the location of such areas is usually unknown prior to optical flow estimation, this is usually performed in three steps. First, occlusion-ignorant optical flow is estimated, then occlusion areas are identified using the estimated (unreliable) optical flow, and, finally, the optical flow is corrected using the computed occlusion areas. This approach, however, does not permit interaction between optical flow and occlusion estimates. In this paper, we permit such interaction by proposing a variational formulation that jointly computes optical flow, implicitly detects occlusions and extrapolates optical flow in occlusion areas. The extrapolation mechanism is based on anisotropic diffusion and uses the underlying image gradient to preserve structure, such as optical flow discontinuities. Our results show significant improvements in the computed optical flow fields over other approaches, both qualitatively and quantitatively. [ABSTRACT FROM AUTHOR]