Binocular Image Flows: Steps Toward Stereo-Motion Fusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Estimation of Object Motion Parameters from Noisy Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Monte Carlo optimization, simulation, and sensitivity of queueing networks
Monte Carlo optimization, simulation, and sensitivity of queueing networks
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Framework for Uncertainty and Validation of 3-D RegistrationMethods Based on Points and Frames
International Journal of Computer Vision
Analysis of Head Pose Accuracy in Augmented Reality
IEEE Transactions on Visualization and Computer Graphics
Optic Flow Field Segmentation and Motion Estimation Using a Robust Genetic Partitioning Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Estimation of pose parameters from a set of least square objective functions
Machine Graphics & Vision International Journal
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The correspondence focuses on the robust 3-D-3-D pose estimation, especially, multiple pose estimation. The robust 3-D-3-D multiple pose estimation problem is formulated as a series of general regressions which involve a successively size-decreasing data set, with each regression relating to one particular pose of interest. Since the first few regressions may carry a severely contaminated Gaussian error noise model, the MF-estimator (Zhuang et al., 1992) is used to solve each regression for each pose of interest. Extensive computer experiments with both real imagery and simulated data are conducted and results are promising. Three distinctive features of the MF-estimator are theoretically discussed and experimentally demonstrated: It is highly robust in the sense that it is not much affected by a possible large portion of outliers or incorrect matches as long as the minimum number of inliers necessary to give a unique solution are provided; It is made virtually independent of initial guesses; It is computationally reasonable and admits an efficient parallel implementation.