{"title":"Optimal dominant motion estimation using adaptive search of transformation space","alternative_title":["LCNS"],"date_updated":"2021-01-12T07:45:06Z","month":"11","doi":"10.1007/978-3-540-74936-3_21","quality_controlled":0,"day":"09","conference":{"name":"DAGM: German Association For Pattern Recognition"},"extern":1,"citation":{"ista":"Ulges A, Lampert C, Keysers D, Breuel T. 2007. Optimal dominant motion estimation using adaptive search of transformation space. DAGM: German Association For Pattern Recognition, LCNS, vol. 4713, 204–213.","mla":"Ulges, Adrian, et al. <i>Optimal Dominant Motion Estimation Using Adaptive Search of Transformation Space</i>. Vol. 4713, Springer, 2007, pp. 204–13, doi:<a href=\"https://doi.org/10.1007/978-3-540-74936-3_21\">10.1007/978-3-540-74936-3_21</a>.","chicago":"Ulges, Adrian, Christoph Lampert, Daniel Keysers, and Thomas Breuel. “Optimal Dominant Motion Estimation Using Adaptive Search of Transformation Space,” 4713:204–13. Springer, 2007. <a href=\"https://doi.org/10.1007/978-3-540-74936-3_21\">https://doi.org/10.1007/978-3-540-74936-3_21</a>.","apa":"Ulges, A., Lampert, C., Keysers, D., &#38; Breuel, T. (2007). Optimal dominant motion estimation using adaptive search of transformation space (Vol. 4713, pp. 204–213). Presented at the DAGM: German Association For Pattern Recognition, Springer. <a href=\"https://doi.org/10.1007/978-3-540-74936-3_21\">https://doi.org/10.1007/978-3-540-74936-3_21</a>","ieee":"A. Ulges, C. Lampert, D. Keysers, and T. Breuel, “Optimal dominant motion estimation using adaptive search of transformation space,” presented at the DAGM: German Association For Pattern Recognition, 2007, vol. 4713, pp. 204–213.","ama":"Ulges A, Lampert C, Keysers D, Breuel T. Optimal dominant motion estimation using adaptive search of transformation space. In: Vol 4713. Springer; 2007:204-213. doi:<a href=\"https://doi.org/10.1007/978-3-540-74936-3_21\">10.1007/978-3-540-74936-3_21</a>","short":"A. Ulges, C. Lampert, D. Keysers, T. Breuel, in:, Springer, 2007, pp. 204–213."},"volume":4713,"date_created":"2018-12-11T12:04:35Z","year":"2007","_id":"3681","publication_status":"published","page":"204 - 213","publisher":"Springer","date_published":"2007-11-09T00:00:00Z","publist_id":"2695","type":"conference","intvolume":"      4713","abstract":[{"lang":"eng","text":"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.\n\nOur 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":[{"first_name":"Adrian","full_name":"Ulges, Adrian","last_name":"Ulges"},{"id":"40C20FD2-F248-11E8-B48F-1D18A9856A87","first_name":"Christoph","last_name":"Lampert","orcid":"0000-0001-8622-7887","full_name":"Christoph Lampert"},{"first_name":"Daniel","last_name":"Keysers","full_name":"Keysers,Daniel"},{"first_name":"Thomas","full_name":"Breuel,Thomas M","last_name":"Breuel"}],"status":"public"}