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A likelihood function for block-based motion analysis

In this paper, the computation of likelihood of block motion candidates is considered. The method is based on the evaluation of the sum of squared differences (SSD) measure for local displacements and probabilistic interpretation of these values using local gradient information. Simulated motion data is used to estimate parameters of conditional SSD distributions. The application of our novel likelihood function is demonstrated in a task of dominant motion estimation, where particle filtering is used to maintain a set of global motion hypotheses. In this task, the block motion likelihood function is used as a basis for hypothesis testing, which provides a means for evaluating global motion hypotheses.