Motion and Structure From Two Perspective Views: Algorithms, Error Analysis, and Error Estimation
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)
Lines and Points in Three Views and the Trifocal Tensor
International Journal of Computer Vision
In Defense of the Eight-Point Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Determining the Epipolar Geometry and its Uncertainty: A Review
International Journal of Computer Vision
Multiple view geometry in computer visiond
Multiple view geometry in computer visiond
Theory of Reconstruction from Image Motion
Theory of Reconstruction from Image Motion
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
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Multiple View Geometry of Projector-Camera Systems from Virtual Mutual Projection
PSIVT '09 Proceedings of the 3rd Pacific Rim Symposium on Advances in Image and Video Technology
Exploiting Mutual Camera Visibility in Multi-camera Motion Estimation
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part I
Synchronized ego-motion recovery of two face-to-face cameras
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
Generating free viewpoint images from mutual projection of cameras
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part I
Full 6DOF pose estimation from geo-located images
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part III
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In this paper, we analyze the computation of epipolar geometry in some special cases where multiple cameras are projected each other in their images. In such cases, epipoles can be obtained directly from images as the projection of cameras. As the result, the epipolar geometry can be computed from less image correspondences with higher stability. In this paper, we analyze the number of corresponding points required for computing bifocal, trifocal and quadrifocal tensors linearly in the case where cameras are projected mutually. We next show a practical linear method for computing multifocal tensors by using the mutual projection of cameras. The degenerate configurations of points and cameras is also studied, and it is shown that some degenerate configurations in general cases are no longer degenerate under the mutual projection of cameras.