Multiple view geometry in computer vision
Multiple view geometry in computer vision
Statistical Optimization for Geometric Computation: Theory and Practice
Statistical Optimization for Geometric Computation: Theory and Practice
Metrischer Trifokaltensor für die Auswertung von Bildfolgen
Mustererkennung 1999, 21. DAGM-Symposium
Statistically Testing Uncertain Geometric Relations
Mustererkennung 2000, 22. DAGM-Symposium
In-strip matching and reconstruction of line segments from UHR aerial image triplets
PIA'11 Proceedings of the 2011 ISPRS conference on Photogrammetric image analysis
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We present a method for estimating unknown geometric entities based on identical, incident, parallel or orthogonal observed entities. These entities can be points and lines in 2D and points, lines and planes in 3D. We don't need any approximate values for the unknowns. The entities are represented as homogeneous vectors or matrices, which leads to an easy formulation for a linear estimation model. Applications of the estimation method are manifold, ranging from 2D corner detection to 3D grouping.