The statistics of optical flow

Computer Vision and Image Understanding

Volumn 82, Issue 1, 2001, Pages 1-32

  Fermüller, Cornelia ^a   Shulman, David ^a   Aloimonos, Yiannis ^a  

^a UNIVERSITY OF MARYLAND   (United States)

Author keywords

Analysis of optical flow estimation algorithms; Bias; Optical illusion

Indexed keywords

ALGORITHMS; ASYMPTOTIC STABILITY; CORRELATION METHODS; FREQUENCY DOMAIN ANALYSIS; LEAST SQUARES APPROXIMATIONS; MAXIMUM LIKELIHOOD ESTIMATION; MOTION ESTIMATION; PATTERN RECOGNITION; PROBABILITY DISTRIBUTIONS;

OPTICAL FLOW ESTIMATION ALGORITHMS; OPTICAL ILLUSIONS;

OPTICAL FLOWS;

EID: 0035308171     PISSN: 10773142     EISSN: None     Source Type: Journal    
DOI: 10.1006/cviu.2000.0900     Document Type: Article

References (48)

1. Spatiotemporal energy models for the perception of motion
2. The extraction of spatiotemporal energy in human and machine vision
3. A computational framework and an algorithm for the measurement of visual motion
4. Multidimensional orientation estimation with applications to texture analysis and optical flow
5. Simultaneous estimation of viewing geometry and structure
6. A parallel algorithm for real-time computation of optical flow
7. Multi-resolution flow through motion analysis
8. Computation of optic flow from the motion of edge features in image sequences
9. Investigations of multigrid algorithms for estimation of optical flow fields in image sequences
10. Direct perception of three-dimensional motion from patterns of visual motion
11. Representations for active vision
12. Visual space distortion
13. The Ouchi illusion as an artifact of biased flow estimation
14. Computation of component velocity from local phase information
15. Estimated true values for errors-in-variables models
16. Estimation in a multivariate "errors in variables" regression model: Large sample results
17. An analysis of the total least squares problem
18. Optical flow using spatiotemporal filters
19. Computations underlying the measurement of visual motion
20. Determining optical flow
21. Estimation with quadratic loss
22. An Iterative image registration technique with an application to stereo vision
23. Towards automatic visual obstacle avoidance
24. Displacement vectors derived from second order intensity variations in image sequences
25. On the estimation of optical flow: Relations between different approaches and some new results
26. An investigation of smoothness constraints for the estimation of displacement vector fields from image sequences
27. Bias-corrected optical flow estimation for road vehicle tracking
28. Probability distributions of optical flow
29. Apparent floating motion in normal and stabilized vision
30. Stochastic pertubation theory
31. Errors in variables for numerical analysis
32. Bayesian modeling of uncertainty in low-level vision
33. Lower level estimation and interpretation of visual motion
34. A computational approach to motion perception
35. Convected activation profiles and receptive fields for real measurements of short range visual motion
36. Robust computation of optical flow in a multi-scale differential framework
37. Sequential hierarchical scene matching