@inproceedings{3701, abstract = {The extraction of a parametric global motion from a motion field is a task with several applications in video processing. We present two probabilistic formulations of the problem and carry out optimization using the RAST algorithm, a geometric matching method novel to motion estimation in video. RAST uses an exhaustive and adaptive search of transformation space and thus gives – in contrast to local sampling optimization techniques used in the past – a globally optimal solution. Among other applications, our framework can thus be used as a source of ground truth for benchmarking motion estimation algorithms. Our main contributions are: first, the novel combination of a state-of-the-art MAP criterion for dominant motion estimation with a search procedure that guarantees global optimality. Second, experimental results that illustrate the superior performance of our approach on synthetic flow fields as well as real-world video streams. Third, a significant speedup of the search achieved by extending the model with an additional smoothness prior.}, author = {Ulges, Adrian and Christoph Lampert and Keysers,Daniel and Breuel,Thomas M}, pages = {204 -- 213}, publisher = {Springer}, title = {{Optimal dominant motion estimation using adaptive search of transformation space}}, doi = {10.1007/978-3-540-74936-3_21}, volume = {4713}, year = {2007}, }