Optimal Estimation of Contour Properties by Cross-Validated Regularization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Motion and Structure From Two Perspective Views: Algorithms, Error Analysis, and Error Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Iterated Estimation of the Motion Parameters of a Rigid Body from Noisy Displacement Vectors
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Study of Methods of Choosing the Smoothing Parameter in Image Restoration by Regularization
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
Geometric Information Criterion for Model Selection
International Journal of Computer Vision
Vehicle-Type Motion Estimation From Multi-Frame Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Human motion estimation from monocular image sequence based on cross-entropy regularization
Pattern Recognition Letters
High-speed target tracking by fuzzy hostility-induced segmentation of optical flow field
Applied Soft Computing
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In this paper, we look at the issue of accurate estimation of the three-dimensional motion parameters of a rigid body from a sequence of synthetic images, and relate the effect of some parameters to the shape of an error function. We first consider the case where only a small set of corresponding points is identified and suggest that a technique called regularization improves the quality and stability of a solution. We then observe that, if more pairs of corresponding points are available, the error function becomes smooth and the solution stable. Finally, we try to improve the quality of estimation by considering more than two consecutive frames for a moving camera looking at a stationary scene, and summing the error functions obtained for any two consecutive frames. Surprisingly enough, this technique does not improve stability unless we use regularization again.