Incremental model-based estimation using geometric constraints

We present a model-based framework for incremental, adaptive object shape estimation and tracking in monocular image sequences. Parametric structure and motion estimation methods usually assume a fixed class of shape representation (splines, deformable superquadrics, etc.) that is initialized prior...

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Bibliographische Detailangaben
Veröffentlicht in:IEEE transactions on pattern analysis and machine intelligence. - 1998. - 27(2005), 5 vom: 15. Mai, Seite 727-38
1. Verfasser: Sminchisescu, Cristian (VerfasserIn)
Weitere Verfasser: Metaxas, Dimitris, Dickinson, Sven
Format: Aufsatz
Sprache:English
Veröffentlicht: 2005
Zugriff auf das übergeordnete Werk:IEEE transactions on pattern analysis and machine intelligence
Schlagworte:Evaluation Study Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S. Validation Study
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245 1 0 |a Incremental model-based estimation using geometric constraints 
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520 |a We present a model-based framework for incremental, adaptive object shape estimation and tracking in monocular image sequences. Parametric structure and motion estimation methods usually assume a fixed class of shape representation (splines, deformable superquadrics, etc.) that is initialized prior to tracking. Since the model shape coverage is fixed a priori, the incremental recovery of structure is decoupled from tracking, thereby limiting both processes in their scope and robustness. In this work, we describe a model-based framework that supports the automatic detection and integration of low-level geometric primitives (lines) incrementally. Such primitives are not explicitly captured in the initial model, but are moving consistently with its image motion. The consistency tests used to identify new structure are based on trinocular constraints between geometric primitives. The method allows not only an increase in the model scope, but also improves tracking accuracy by including the newly recovered features in its state estimation. The formulation is a step toward automatic model building, since it allows both weaker assumptions on the availability of a prior shape representation and on the number of features that would otherwise be necessary for entirely bottom-up reconstruction. We demonstrate the proposed approach on two separate image-based tracking domains, each involving complex 3D object structure and motion 
650 4 |a Evaluation Study 
650 4 |a Journal Article 
650 4 |a Research Support, Non-U.S. Gov't 
650 4 |a Research Support, U.S. Gov't, Non-P.H.S. 
650 4 |a Validation Study 
700 1 |a Metaxas, Dimitris  |e verfasserin  |4 aut 
700 1 |a Dickinson, Sven  |e verfasserin  |4 aut 
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