1 Introduction. - 1. 1 Modeling uncertainty in low-level vision. - 1. 2 Previous work. - 1. 3 Overview of results. - 1. 4 Organization. - 2 Representations for low-level vision. - 2. 1 Visible surface representations. - 2. 2 Visible surface algorithms. - 2. 3 Multiresolution representations. - 2. 4 Discontinuities. - 2. 5 Alternative representations. - 3 Bayesian models and Markov Random Fields. - 3. 1 Bayesian models. - 3. 2 Markov Random Fields. - 3. 3 Using probabilistic models. - 4 Prior models. - 4. 1 Regularization and fractal priors. - 4. 2 Generating constrained fractals. - 4. 3 Relative depth representations (reprise). - 4. 4 Mechanical vs. probabilistic models. - 5 Sensor models. - 5. 1 Sparse data: spring models. - 5. 2 Sparse data: force field models. - 5. 3 Dense data: optical flow. - 5. 4 Dense data: image intensities. - 6 Posterior estimates. - 6. 1 MAP estimation. - 6. 2 Uncertainty estimation. - 6. 3 Regularization parameter estimation. - 6. 4 Motion estimation without correspondence. - 7 Incremental algorithms for depth-from-motion. - 7. 1 Kaiman filtering. - 7. 2 Incremental iconic depth-from-motion. - 7. 3 Joint modeling of depth and intensity. - 8 Conclusions. - 8. 1 Summary. - 8. 2 Future research. - A Finite element implementation. - B Fourier analysis. - B. 1 Filtering behavior of regularization. - B. 2 Fourier analysis of the posterior distribution. - B. 3 Analysis of gradient descent. - B. 4 Finite element solution. - B. 5 Fourier analysis of multigrid relaxation. - C Analysis of optical flow computation. - D Analysis of parameter estimation. - D. 1 Computing marginal distributions. - D. 2 Bayesian estimation equations. - D. 3 Likelihood of observations. - Table of symbols. Language: English