IEEE Transactions on Pattern Analysis and Machine Intelligence
Matrix computations (3rd ed.)
In Defense of the Eight-Point Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
Invariant Fitting of Two View Geometry
IEEE Transactions on Pattern Analysis and Machine Intelligence
Direct type-specific conic fitting and eigenvalue bias correction
Image and Vision Computing
Sampling Minimal Subsets with Large Spans for Robust Estimation
International Journal of Computer Vision
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Computer Vision theory is firmly rooted in Projective Geometry, whereby geometric objects can be effectively modeled by homogeneous vectors. We begin from Gauss's 200 year old theorem of least squares to derive a generic algorithm for the direct estimation of homogeneous vectors. We uncover the common link of previous methods, showing that direct estimation is not an ill-conditioned problem as is the popular belief, but has merely been an ill-solved problem. Results show improvements in goodness-of-fit and numerical stability, and demonstrate that “data normalization” is unnecessary for a well-founded algorithm.