Multiresolution Gaussian Mixture Models for Visual Motion Estimation

R Wilson, A Calway

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

2 Citations (Scopus)

Abstract

This paper introduces a new generalisation of scale-space and pyramids, which combines statistical modelling with a spatial representation. The representation uses the familiar concept of multiple resolutions, but applied to a Gaussian mixture representation of the image - hence the title MGMM. It is shown that MGMM can approximate any probability density and can adapt to smooth motions. After a brief presentation of the theory, it is shown how MGMM can be applied to the estimation of visual motion.
Translated title of the contributionMultiresolution Gaussian Mixture Models for Visual Motion Estimation
Original languageEnglish
Title of host publicationUnknown
EditorsI. Pitas
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages921 - 924
Number of pages3
ISBN (Print)0780367278
Publication statusPublished - Oct 2001

Bibliographical note

Conference Proceedings/Title of Journal: Proceedings of the IEEE International Conference on Image Processing

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