Nonlinear Estimation of the Fundamental Matrix with Minimal Parameters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Estimation of Nonlinear Errors-in-Variables Models for Computer Vision Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Calibrating distributed camera networks using belief propagation
EURASIP Journal on Applied Signal Processing
Determining an initial image pair for fixing the scale of a 3d reconstruction from an image sequence
DAGM'06 Proceedings of the 28th conference on Pattern Recognition
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This paper presents a consistent theory for describing indeterminacy and uncertainty of three-dimensional (3-D) reconstruction from a sequence of images. First, we give a group-theoretical analysis of gauges and gauge transformations. We then discuss how to evaluate the reliability of the solution that has indeterminacy and extend the Cramer-Rao lower bound to incorporate internal indeterminacy. We also introduce the free-gauge approach and define the normal form of a covariance matrix that is independent of particular gauges. Finally, we show simulated and real-image examples to illustrate the effect of gauge freedom on uncertainty description