IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optical Flow Estimation: An Error Analysis of Gradient-Based Methods with Local Optimization
IEEE Transactions on Pattern Analysis and Machine Intelligence
A General Aperture Problem for Direct Estimation of 3-D Motion Parameters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Obstacle Avoidance Using Flow Field Divergence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Derivation of qualitative information in motion analysis
Image and Vision Computing - Special issue on the first ECCV 1990
Measurement of Visual Motion
Structure-from-Motion under Orthographic Projection
ECCV '90 Proceedings of the First European Conference on Computer Vision
The Analysis of time varying image sequences
ECCV '90 Proceedings of the First European Conference on Computer Vision
ECCV '90 Proceedings of the First European Conference on Computer Vision
The computation of optical flow
ACM Computing Surveys (CSUR)
On the Fourier Properties of Discontinuous Motion
Journal of Mathematical Imaging and Vision
Robust, Real-Time Motion Estimation from Long Image Sequences Using Kalman Filtering
BMVC '00 Proceedings of the First IEEE International Workshop on Biologically Motivated Computer Vision
ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol.2)-Volume 2 - Volume 2
Recursive estimation of time-varying motion and structure parameters
Pattern Recognition
Optimal-flow minimum-cost correspondence assignment in particle flow tracking
Computer Vision and Image Understanding
Adjustable linear models for optic flow based obstacle avoidance
Computer Vision and Image Understanding
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The accuracy and the dependence on parameters of a general scheme for the analysis of time-varying image sequences are discussed. The approach is able to produce vector fields from which it is possible to recover 3-D motion parameters such as time-to-collision and angular velocity. The numerical stability of the computed optical flow and the dependence of the recovery of 3-D motion parameters on spatial and temporal filtering is investigated. By considering optical flows computed on subsampled images or along single scanlines, it is also possible to recover 3-D motion parameters from reduced optical flows. An adequate estimate of time-to-collision can be obtained from sequences of images with spatial resolution reduced to 128*128 pixels or from sequences of single scanlines passing near the focus of expansion. The use of Kalman filtering increases the accuracy and the robustness of the estimation of motion parameters. The proposed approach seems to be able to provide not only a theoretical background but also practical tools that are adequate for the analysis of time-varying image sequences.