{"intvolume":"      4713","alternative_title":["LNCS"],"doi":"10.1007/978-3-540-74936-3_21","quality_controlled":0,"day":"09","year":"2007","_id":"3701","page":"204 - 213","title":"Optimal dominant motion estimation using adaptive search of transformation space","date_created":"2018-12-11T12:04:42Z","date_updated":"2021-01-12T07:51:35Z","author":[{"last_name":"Ulges","first_name":"Adrian","full_name":"Ulges, Adrian"},{"first_name":"Christoph","last_name":"Lampert","orcid":"0000-0001-8622-7887","id":"40C20FD2-F248-11E8-B48F-1D18A9856A87","full_name":"Christoph Lampert"},{"last_name":"Keysers","first_name":"Daniel","full_name":"Keysers,Daniel"},{"last_name":"Breuel","first_name":"Thomas","full_name":"Breuel,Thomas M"}],"conference":{"name":"DAGM: German Association For Pattern Recognition"},"date_published":"2007-11-09T00:00:00Z","status":"public","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."}],"month":"11","publisher":"Springer","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, LNCS, 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>.","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>","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.","short":"A. Ulges, C. Lampert, D. Keysers, T. Breuel, in:, Springer, 2007, pp. 204–213."},"volume":4713,"extern":1,"publication_status":"published","type":"conference","publist_id":"2656"}