DocumentCode
1742716
Title
MGMM: multiresolution Gaussian mixture models for computer vision
Author
Wilson, Roland
Author_Institution
Warwick Univ., Coventry, UK
Volume
1
fYear
2000
fDate
2000
Firstpage
212
Abstract
Introduces a 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. Examples show how MGMM can be applied to problems such as segmentation and motion analysis
Keywords
computer vision; image motion analysis; image segmentation; probability; multiresolution Gaussian mixture models; probability density; pyramids; scale-space; spatial representation; statistical modelling; Computer vision; Frequency domain analysis; Image coding; Image motion analysis; Image representation; Image resolution; Image segmentation; Motion analysis; Probability; Spatial resolution;
fLanguage
English
Publisher
ieee
Conference_Titel
Pattern Recognition, 2000. Proceedings. 15th International Conference on
Conference_Location
Barcelona
ISSN
1051-4651
Print_ISBN
0-7695-0750-6
Type
conf
DOI
10.1109/ICPR.2000.905305
Filename
905305
Link To Document