Simultaneous Motion Estimation and Segmentation 1
نویسندگان
چکیده
We present a Bayesian framework that combines motion (optical ow) estimation and segmentation based on a representation of the motion eld as the sum of a parametric eld and a residual eld. The parameters describing the parametric component are found by a least squares procedure given the best estimates of the motion and segmentation elds. The motion eld is updated by estimating the minimum-norm residual eld given the best estimate of the parametric eld, under the constraint that motion eld be smooth within each segment. The segmentation eld is updated to yield the minimum-norm residual eld given the best estimate of the motion eld, using Gibbsian priors. The solution to successive optimization problems are obtained using the highest conndence rst (HCF) or iterated conditional mode (ICM) optimization methods. Experimental results on real video are shown.
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