Estimation of Object Motion Parameters from Noisy Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Least-Squares Fitting of Two 3-D Point Sets
IEEE Transactions on Pattern Analysis and Machine Intelligence
Camera rotation invariance of image characteristics
Computer Vision, Graphics, and Image Processing
Constraints on length and angle
Computer Vision, Graphics, and Image Processing
Transformation of Optical Flow by Camera Rotation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A simplification to linear two-view motion algorithms
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
Some Properties of the E Matrix in Two-View Motion Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fitting ellipses and predicting confidence envelopes using a bias corrected Kalman filter
Image and Vision Computing - Special issue: 5th Alvey vision meeting
IEEE Transactions on Pattern Analysis and Machine Intelligence
Motion from point matches: multiple of solutions
International Journal of Computer Vision
An Iterated Estimation of the Motion Parameters of a Rigid Body from Noisy Displacement Vectors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Polynomial Methods for Structure from Motion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Least-Squares Estimation of Transformation Parameters Between Two Point Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Estimation Three-Dimensional Motion of Rigid Objects from Noisy Observations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Estimating the Kinematics and Structure of a Rigid Object from a Sequence of Monocular Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
A multi-frame approach to visual motion perception
International Journal of Computer Vision
A Kalman filter approach for accurate 3-D motion estimation from a sequence of stereo images
CVGIP: Image Understanding
Time-varying images: the effect of finite resolution on uniqueness
CVGIP: Image Understanding
Computational projective geometry
CVGIP: Image Understanding
Geometric computation for machine vision
Geometric computation for machine vision
Group Theoretical Methods in Image Understanding
Group Theoretical Methods in Image Understanding
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Multi-Frame Structure-from-Motion Algorithm under Perspective Projection
International Journal of Computer Vision - Special issue on computer vision research at NEC Research Institute
Heteroscedastic Regression in Computer Vision: Problems with Bilinear Constraint
International Journal of Computer Vision - Special issue on a special section on visual surveillance
On the Fitting of Surfaces to Data with Covariances
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical Bias of Conic Fitting and Renormalization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Analysis of 3-D Rotation Fitting
IEEE Transactions on Pattern Analysis and Machine Intelligence
Correction of bias for motion estimation algorithms
Pattern Recognition Letters
Stochastic Approximation and Rate-Distortion Analysis for Robust Structure and Motion Estimation
International Journal of Computer Vision
Journal of Mathematical Imaging and Vision
Performance characterization in computer vision: A guide to best practices
Computer Vision and Image Understanding
Epiflow-A paradigm for tracking stereo correspondences
Computer Vision and Image Understanding
Error Analysis in Homography Estimation by First Order Approximation Tools: A General Technique
Journal of Mathematical Imaging and Vision
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The problem of estimating 3D rigid motion from point correspondences over two views is formulated as nonlinear least-squares (LS) optimization, and the statistical behaviors of the errors in the solution are analyzed by introducing a realistic model of noise described in terms of the covariance matrices of N-vectors. It is shown that the LS solution based on the epipolar constraint is statistically biased. The geometry of this bias is described in both quantitative and qualitative terms. Finally, an unbiased estimation scheme is presented, and random number simulations are conducted to observe its effectiveness.