Missing data which inevitably occurs in observed time series may lead to an erroneous result based on the correlation integral analysis. Effects of data, missing at regular and irregular times, on the analyzed result are estimated. A model estimation is obtained for the Lorenz time series. The effects of the missing data in economic and astronomical time series are estimated using the correlation integral analysis. A convenient method of choosing a time lag is proposed to minimize the effect of regularly missing data.

Feature point tracking over a video sequence fails when the points go out of the field of view or behind other objects. In this paper, we extend such interrupted tracking by imposing the constraint that under the affine camera model all feature trajectories should be in an affine space. Our method consists of iterations for optimally extending the trajectories and for optimally estimating the affine space, coupled with an outlier removal process. Using real video images, we demonstrate that our method can restore a sufficient number of trajectories for detailed 3-D reconstruction.