Weiler, Daniel ; Willert, Volker ; Eggert, Julian (2008)
A Probabilistic Prediction Method for Object Contour Tracking.
18th International Conference on Artificial Neural Networks. Prague, Czech Republic (03.-06.09.2008)
doi: 10.1007/978-3-540-87536-9_103
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
In this paper we present an approach for probabilistic contour prediction in an object tracking system. We combine level-set methods for image segmentation with optical flow estimations based on probability distribution functions (pdf’s) calculated at each image position. Unlike most recent level-set methods that consider exclusively the sign of the level-set function to determine an object and its background, we introduce a novel interpretation of the value of the level-set function that reflects the confidence in the contour. To this end, in a sequence of consecutive images, the contour of an object is transformed according to the optical flow estimation and used as the initial object hypothesis in the following image. The values of the initial level-set function are set according to the optical flow pdf’s and thus provide an opportunity to incorporate the uncertainties of the optical flow estimation in the object contour prediction.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2008 |
| Autor(en): | Weiler, Daniel ; Willert, Volker ; Eggert, Julian |
| Art des Eintrags: | Bibliographie |
| Titel: | A Probabilistic Prediction Method for Object Contour Tracking |
| Sprache: | Englisch |
| Publikationsjahr: | 25 August 2008 |
| Verlag: | Springer |
| Buchtitel: | Artificial Neural Networks - ICANN 2008 |
| Reihe: | Lecture Notes in Computer Science |
| Band einer Reihe: | 5163 |
| Veranstaltungstitel: | 18th International Conference on Artificial Neural Networks |
| Veranstaltungsort: | Prague, Czech Republic |
| Veranstaltungsdatum: | 03.-06.09.2008 |
| DOI: | 10.1007/978-3-540-87536-9_103 |
| Kurzbeschreibung (Abstract): | In this paper we present an approach for probabilistic contour prediction in an object tracking system. We combine level-set methods for image segmentation with optical flow estimations based on probability distribution functions (pdf’s) calculated at each image position. Unlike most recent level-set methods that consider exclusively the sign of the level-set function to determine an object and its background, we introduce a novel interpretation of the value of the level-set function that reflects the confidence in the contour. To this end, in a sequence of consecutive images, the contour of an object is transformed according to the optical flow estimation and used as the initial object hypothesis in the following image. The values of the initial level-set function are set according to the optical flow pdf’s and thus provide an opportunity to incorporate the uncertainties of the optical flow estimation in the object contour prediction. |
| Zusätzliche Informationen: | Proceedings, Part I |
| Fachbereich(e)/-gebiet(e): | 18 Fachbereich Elektrotechnik und Informationstechnik 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Automatisierungstechnik und Mechatronik 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Automatisierungstechnik und Mechatronik > Regelungsmethoden und Robotik (ab 01.08.2022 umbenannt in Regelungsmethoden und Intelligente Systeme) |
| Hinterlegungsdatum: | 16 Aug 2010 14:32 |
| Letzte Änderung: | 11 Mai 2023 08:08 |
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