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Image analysis and segmentation using mixture models

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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:

2000