Abstract
This paper describes a novel application of Statistical Learning Theory (SLT) for motion prediction. SLT provides analytical VC-generalization bounds for model selection; these bounds relate unknown prediction risk (generalization performance) and known quantities such as the number of training samples, empirical error, and a measure of model complexity called the VC-dimension. We use the VC-generalization bounds for the problem of choosing optimal motion models from small sets of image measurements (flow). We present results of experiments on image sequences for motion interpolation and extrapolation; these results demonstrate the strengths of our approach.
Original language | English (US) |
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Pages (from-to) | 151-154 |
Number of pages | 4 |
Journal | Proceedings - International Conference on Pattern Recognition |
Volume | 16 |
Issue number | 1 |
State | Published - 2002 |