Projective Reconstruction and Invariants from Multiple Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Trilinearity in visual recognition by alignment
ECCV '94 Proceedings of the third European conference on Computer vision (vol. 1)
Canonic representations for the geometries of multiple projective views
ECCV '94 Proceedings of the third European conference on Computer vision (vol. 1)
A common framework for kinetic depth, reconstruction and motion for deformable objects
ECCV '94 Proceedings of the third European conference on Computer Vision (Vol. II)
Theory of Reconstruction from Image Motion
Theory of Reconstruction from Image Motion
Camera Self-Calibration: Theory and Experiments
ECCV '92 Proceedings of the Second European Conference on Computer Vision
What can be seen in three dimensions with an uncalibrated stereo rig
ECCV '92 Proceedings of the Second European Conference on Computer Vision
Depth Computations from Polyhedral Images
ECCV '92 Proceedings of the Second European Conference on Computer Vision
The Key to Three-View Geometry
International Journal of Computer Vision
Geometrical computer vision from chasles to today
SCIA'05 Proceedings of the 14th Scandinavian conference on Image Analysis
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This paper deals with the problem of reconstructing the locations ofn points in space from m different images without cameracalibration. It shows how these problems can be put into a similartheoretical framework.A new concept, the reduced fundamental matrix, is introduced. Itcontains just 4 parameters and can be used to predict locations ofpoints in the images and to make reconstruction. We also introducethe concept of reduced fundamental tensor, which describes therelations between points in 3 images. It has 15 components anddepends on 9 parameters. Necessary and sufficient conditions for atensor to be a reduced fundamental tensor are derived. This frameworkcan be generalised to a sequence of images. The dependencies betweenthe different representations are investigated. Furthermore acanonical form of the camera matrices in a sequence are presented.