Multimodal motion estimation and segmentation using Markov random fields
- 4 December 2002
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. i, 378-383
- https://doi.org/10.1109/icpr.1990.118132
Abstract
A multimodal approach to the problem of velocity estimation is presented. It combines the advantages of the feature-based and gradient-based methods by making them cooperate in a single global motion estimator. The theoretical framework is based on global Bayesian decision associated with Markov random field models. The proposed approach addresses, in parallel, the problem of velocity estimation and segmentation. Results on synthetic as well as on real-world image sequences are presented. Accurate motion measurement and detection of motion discontinuities with a surprisingly good quality have been obtained.Keywords
This publication has 10 references indexed in Scilit:
- Motion estimation and segmentation using a global Bayesian approachPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- A maximum likelihood framework for determining moving edgesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1989
- Motion field and optical flow: qualitative propertiesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1989
- On the computation of motion from sequences of images-A reviewProceedings of the IEEE, 1988
- Using Canny's criteria to derive a recursively implemented optimal edge detectorInternational Journal of Computer Vision, 1987
- An Investigation of Smoothness Constraints for the Estimation of Displacement Vector Fields from Image SequencesIEEE Transactions on Pattern Analysis and Machine Intelligence, 1986
- The image flow constraint equationComputer Vision, Graphics, and Image Processing, 1986
- On the Statistical Analysis of Dirty PicturesJournal of the Royal Statistical Society Series B: Statistical Methodology, 1986
- Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of ImagesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1984
- Determining optical flowArtificial Intelligence, 1981