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Multiresolution Gaussian mixture models : theory and applications

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Wilson, Roland (2000) Multiresolution Gaussian mixture models : theory and applications. University of Warwick. Department of Computer Science. (Department of Computer Science research report). (Unpublished)

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Abstract

This paper introduces a new generalisation of scale-space and multiresolution 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 Multiresolution Gaussian Mixture Model (MGMM). It is shown that MGMM can approximate any probability density and can deal with the smooth motions that typically occur in image analysis and vision. An efficient recursive algorithm for computing MGMM representations, based on Monte Carlo Markov Chain methods, is presented. After a brief presentation of the theory, examples are used to show how MGMM can be applied to vision problems such as segmentation, stereopsis and motion analysis.

Item Type: Report
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Divisions: Faculty of Science, Engineering and Medicine > Science > Computer Science
Library of Congress Subject Headings (LCSH): Image processing -- Digital techniques, Gaussian processes
Series Name: Department of Computer Science research report
Publisher: University of Warwick. Department of Computer Science
Official Date: 28 February 2000
Dates:
DateEvent
28 February 2000Completion
Number: Number 371
Number of Pages: 27
DOI: CS-RR-371
Institution: University of Warwick
Theses Department: Department of Computer Science
Status: Not Peer Reviewed
Publication Status: Unpublished
Access rights to Published version: Restricted or Subscription Access
Funder: Engineering and Physical Sciences Research Council (EPSRC)
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