Comutations underlying the measuremnt of visual motion.
Artificial Intelligence
Estimation of Object Motion Parameters from Noisy Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Inherent Ambiguities in Recovering 3-D Motion and Structure from a Noisy Flow Field
IEEE Transactions on Pattern Analysis and Machine Intelligence
Integration of visual modules: an extension of the Marr paradigm
Integration of visual modules: an extension of the Marr paradigm
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
Measurement of Visual Motion
Robot Vision
Shape Ambiguities in Structure From Motion
IEEE Transactions on Pattern Analysis and Machine Intelligence
A General Motion Model and Spatio-Temporal Filters forComputing Optical Flow
International Journal of Computer Vision
Geometric Information Criterion for Model Selection
International Journal of Computer Vision
Geometry of Distorted Visual Space and Cremona Transformation
International Journal of Computer Vision
A Multi-Frame Structure-from-Motion Algorithm under Perspective Projection
International Journal of Computer Vision - Special issue on computer vision research at NEC Research Institute
Point Light Source Estimation from Two Images and Its Limits
International Journal of Computer Vision
International Journal of Computer Vision - Special issue on image-based servoing
Extracting Structure from Optical Flow Using the Fast Error Search Technique
International Journal of Computer Vision
Optimal Structure from Motion: Local Ambiguities and Global Estimates
International Journal of Computer Vision
Characterizing Depth Distortion under Different Generic Motions
International Journal of Computer Vision
Order Parameters for Detecting Target Curves in Images: When Does High Level Knowledge Help?
International Journal of Computer Vision - Special issue on statistical and computational theories of vision: Part II
Understanding the Behavior of SFM Algorithms: A Geometric Approach
International Journal of Computer Vision
Optic Flow Field Segmentation and Motion Estimation Using a Robust Genetic Partitioning Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optimal Structure from Motion: Local Ambiguities and Global Estimates
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Stochastic Approximation and Rate-Distortion Analysis for Robust Structure and Motion Estimation
International Journal of Computer Vision
Face reconstruction from monocular video using uncertainty analysis and a generic model
Computer Vision and Image Understanding - Special issue on Face recognition
Structure from Motion Using Sequential Monte Carlo Methods
International Journal of Computer Vision
Depth distortion under calibration uncertainty
Computer Vision and Image Understanding
The least-squares error for structure from infinitesimal motion
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical Methods and Models for Video-Based Tracking, Modeling, and Recognition
Foundations and Trends in Signal Processing
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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The inherent ambiguities in recovering 3-D motion information from a single optical flow field are studied using a statistical model. The ambiguities are quantified using the Cramer-Rao lower bound. As a special case, the performance bound for the motion of 3-D rigid planar surfaces is studied in detail. The dependence of the bound on factors such as the underlying motion, surface position, surface orientation, field of view, and density of available pixels are derived as closed-form expressions. A subset of the results support S. Adiv's (1989) analysis of the inherent ambiguities of motion parameters. For the general motion of an arbitrary surface. It is shown that the aperture problem in computing the optical flow restricts the nontrivial information about the 3-D motion to a sparse set of pixels at which both components of the flow velocity are observable. Computer simulations are used to study the dependence of the inherent ambiguities on the underlying motion, the field of view, and the number of feature points for the motion in front of a nonplanar environment.