Least-Squares Fitting of Two 3-D Point Sets
IEEE Transactions on Pattern Analysis and Machine Intelligence
Parameter estimation and hypothesis testing in linear models
Parameter estimation and hypothesis testing in linear models
Statistically Testing Uncertain Geometric Relations
Mustererkennung 2000, 22. DAGM-Symposium
Uncertain Projective Geometry: Statistical Reasoning For Polyhedral Object Reconstruction (Lecture Notes in Computer Science)
Pose Estimation in Conformal Geometric Algebra Part I: The Stratification of Mathematical Spaces
Journal of Mathematical Imaging and Vision
Computing regions of interest for geometric features in digital images
Discrete Applied Mathematics
Recursive estimation with implicit constraints
Proceedings of the 29th DAGM conference on Pattern recognition
Stochastically optimal epipole estimation in omnidirectional images with geometric algebra
RobVis'08 Proceedings of the 2nd international conference on Robot vision
Transformation polytopes for line correspondences in digital images
IWCIA'08 Proceedings of the 12th international conference on Combinatorial image analysis
Pose estimation from uncertain omnidirectional image data using line-plane correspondences
DAGM'06 Proceedings of the 28th conference on Pattern Recognition
DAGM'06 Proceedings of the 28th conference on Pattern Recognition
Geometry and kinematics with uncertain data
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
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In this text we show how points, point pairs, lines, planes, circles, spheres, and rotation, translation and dilation operators and their uncertainty can be evaluated from uncertain data in a unified manner using the Geometric Algebra of conformal space. This extends previous work by Förstner et al. [3] from points, lines and planes to non-linear entities and operators, while keeping the linearity of the estimation method. We give a theoretical description of our approach and show the results of some synthetic experiments.