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 contribution||Multiresolution Gaussian Mixture Models for Visual Motion Estimation|
|Title of host publication||Unknown|
|Publisher||Institute of Electrical and Electronics Engineers (IEEE)|
|Pages||921 - 924|
|Number of pages||3|
|Publication status||Published - Oct 2001|