Trilinearity in visual recognition by alignment
ECCV '94 Proceedings of the third European conference on Computer vision (vol. 1)
In Defense of the Eight-Point Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Algebraic Functions For Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Estimation of Relative Camera Positions for Uncalibrated Cameras
ECCV '92 Proceedings of the Second European Conference on Computer Vision
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
On the geometry and algebra of the point and line correspondences between N images
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Trilinearity of three perspective views and its associated tensor
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Matching constraints and the joint image
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
Point matching constraints in two and three views
Proceedings of the 29th DAGM conference on Pattern recognition
Sparse motion segmentation using multiple six-point consistencies
ACCV'10 Proceedings of the 2010 international conference on Computer vision - Volume Part I
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A single-view matching constraint is described which represents a necessary condition which 6 points in an image must satisfy if they are the images of 6 known 3D points under an arbitrary projective transformation. Similar to the well-known matching constrains for two or more view, represented by fundamental matrices or trifocal tensors, single-view matching constrains are represented by tensors and when multiplied with the homogeneous image coordinates the result vanishes when the condition is satisfied. More precisely, they are represented by 6-th order tensors on R3 which can be computed in a simple manner from the camera projection matrix and the 6 3D points. The single-view matching constraints can be used for finding correspondences between detected 2D feature points and known 3D points, e.g., on an object, which are observed from arbitrary views. Consequently, this type of constraint can be said to be a representation of 3D shape (in the form of a point set) which is invariant to projective transformations when projected onto a 2D image.