By Topic

Image analysis and segmentation using mixture models

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $31
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

1 Author(s)
Wilson, R. ; Dept. of Comput. Sci., Warwick Univ., Coventry, UK

This paper combines statistical modelling with a spatial representation for image analysis. The representation uses the familiar concept of multiple resolutions, but applied to a Gaussian mixture representation of the image: Multiresolution Gaussian Mixture Models (MGMM). It is shown that MGMM can approximate any probability density and can be efficiently computed. After a brief presentation of the theory, examples are used to show how MGMM can be applied to segmentation and motion analysis

Published in:

Time-scale and Time-Frequency Analysis and Applications (Ref. No. 2000/019), IEE Seminar on

Date of Conference: