On the estimation of dense displacement vector fields from image sequences
Proc. of the ACM SIGGRAPH/SIGART interdisciplinary workshop on Motion: representation and perception
Investigations of multigrid algorithms for the estimation of optical flow fieldsin image sequences
Computer Vision, Graphics, and Image Processing
Optical Flow with an Intensity-Weighted Smoothing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Local and Global Interpretation of Moving Images
Local and Global Interpretation of Moving Images
Measurement of Visual Motion
Data Fusion for Sensory Information Processing Systems
Data Fusion for Sensory Information Processing Systems
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optical-flow based on an edge-avoidance procedure
Computer Vision and Image Understanding
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Many applications of visual motion, such as navigation and tracking, require that image flow be estimated in an on-line, incremental fashion. Kalman filtering provides a robust and efficient mechanism for recording image-flow estimates along with their uncertainty and for integrating new measurements with the existing estimates. In this paper, the fundamental form of motion information in time-varying imagery-conservation information-is recovered along with its uncertainty from a pair of images using a correlation-based approach. As more images are acquired, this information is integrated temporally and spatially using a Kalman filter. The uncertainty in the estimates decreases with the progress of time. This framework is shown to behave very well at the discontinuities of the flow field. Algorithms based on this framework are used to recover image flow from a variety of image sequences.