Computer Vision, Graphics, and Image Processing
Motion and Structure From Two Perspective Views: Algorithms, Error Analysis, and Error Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Invariants of Six Points and Projective Reconstruction From Three Uncalibrated Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple view geometry in computer vision
Multiple view geometry in computer vision
A Geometric Approach for the Theory and Applications of 3D Projective Invariants
Journal of Mathematical Imaging and Vision
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Sketchpad: A man-machine graphical communication system (Outstanding dissertations in the computer sciences)
Globally Optimal Estimates for Geometric Reconstruction Problems
International Journal of Computer Vision
A unified and complete framework of invariance for six points
IWMM'04/GIAE'04 Proceedings of the 6th international conference on Computer Algebra and Geometric Algebra with Applications
The invariant representations of a quadric cone and a twisted cubic
IEEE Transactions on Pattern Analysis and Machine Intelligence
Equidistant (fθ) fish-eye perspective with application in distortion centre estimation
Image and Vision Computing
Degeneracy from twisted cubic under two views
Journal of Computer Science and Technology
Twisted cubic: degeneracy degree and relationship with general degeneracy
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part II
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The popularly used DLT method sometimes fails to give reliable camera parameter estimation. It is therefore important to detect the unreliability and provide the corresponding solutions. Based on a complete framework of invariance for six points, we construct two evaluation functions to detect the unreliability. The two evaluation functions do not involve any computations for the camera projective matrix or optical center and thus are efficient to perform the detection. Then, the guidelines corresponding to the different detection results are presented. In particular, a filtering RANSAC method to remove the detected unreliable points is provided. The filtering RANSAC proves to be successful in removing the unreliable points even if these points are of a large proportion.