Inherent Ambiguities in Recovering 3-D Motion and Structure from a Noisy Flow Field
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
Subspace methods for recovering rigid motion I: algorithm and implementation
International Journal of Computer Vision
Statistical Analysis of Inherent Ambiguities in Recovering 3-D Motion from a Noisy Flow Field
IEEE Transactions on Pattern Analysis and Machine Intelligence
3-D interpretation of optical flow by renormalization
International Journal of Computer Vision
Models of statistical visual motion estimation
CVGIP: Image Understanding
Shape Ambiguities in Structure From Motion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Effects of errors in the viewing geometry on shape estimation
Computer Vision and Image Understanding
Geometry of Distorted Visual Space and Cremona Transformation
International Journal of Computer Vision
Linear Differential Algorithm for Motion Recovery: AGeometric Approach
International Journal of Computer Vision
International Journal of Computer Vision - Special issue on image-based servoing
A critique of structure-from-motion algorithms
Computer Vision and Image Understanding
Optimal Structure from Motion: Local Ambiguities and Global Estimates
International Journal of Computer Vision
The statistics of optical flow
Computer Vision and Image Understanding
Characterizing Depth Distortion under Different Generic Motions
International Journal of Computer Vision
Optimization Criteria and Geometric Algorithms for Motion and Structure Estimation
International Journal of Computer Vision
Theory of Reconstruction from Image Motion
Theory of Reconstruction from Image Motion
Motion and Structure from Image Sequences
Motion and Structure from Image Sequences
A New Structure-from-Motion Ambiguity
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Techniques for the Estimation of Structure from Motion in the Uncalibrated Case
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
Critical Motion Sequences for Monocular Self-Calibration and Uncalibrated Euclidean Reconstruction
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Optimal Structure from Motion: Local Ambiguities and Global Estimates
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Generalized image matching by the method of differences
Generalized image matching by the method of differences
Understanding the Relationship Between the Optimization Criteria in Two-View Motion Analysis
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
The least-squares error for structure from infinitesimal motion
International Journal of Computer Vision
Fixation as a Mechanism for Stabilization of Short Image Sequences
International Journal of Computer Vision
Optimal instantaneous rigid motion estimation insensitive to local minima
Computer Vision and Image Understanding
Motion bias and structure distortion induced by intrinsic calibration errors
Image and Vision Computing
Optic flow from unstable sequences through local velocity constancy maximization
Image and Vision Computing
Linear Quasi-Parallax SfM Using Laterally-Placed Eyes
International Journal of Computer Vision
When Discrete Meets Differential
International Journal of Computer Vision
Behaviour of SFM algorithms with erroneous calibration
Computer Vision and Image Understanding
Error characteristics of SFM with erroneous focal length
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part I
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We put forth in this paper a geometrically motivated motion error analysis which is capable of supporting investigation of global effect such as inherent ambiguities. This is in contrast with the usual statistical kinds of motion error analyses which can only deal with local effect such as noise perturbations, and where much of the results regarding global ambiguities are empirical in nature. The error expression that we derive allows us to predict the exact conditions likely to cause ambiguities and how these ambiguities vary with motion types such as lateral or forward motion. Given the erroneous 3-D motion estimates caused by the inherent ambiguities, it is also important to study the behavior of the resultant distortion in depth recovered under different motion-scene configurations. Such an investigation may alert us to the occurrence of ambiguities under different conditions and be more careful in picking the solution. Our formulation, though geometrically motivated, was also put to use in modeling the effect of noise and in revealing the strong influence of feature distribution. Experiments on both synthetic and real image sequences were conducted to verify the various theoretical predictions.