Applications of high dimensionalmodel representations to computer vision

Authors:
Emre Demiralp
Affiliations:
Department of Psychology, University of Michigan
Venue:
WSEAS Transactions on Mathematics
Year:
2009

Citing 10
Cited 10

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Abstract

A new and powerful method for matrix decomposition is developed in this work. It is similar to singular value decomposition and the main idea comes from the univariate approximation of a function, given on a planar grid's nodes, by two variable high dimensional model representation. The proposed method is less iteration dependent than the singular value decomposition and the components are determined via straightforward steps containing recursions. It seems to have more capabilities than the singular value decomposition as an alternative method. An illustrative application is also given.