Self-similar random vector fields and their wavelet analysis | Zendy

Pouya D. Tafti | Zendy; Michaël Unser | Zendy

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Self-similar random vector fields and their wavelet analysis

Author(s) -

Pouya D. Tafti,

Michaël Unser

Publication year - 2009

Publication title -

proceedings of spie, the international society for optical engineering/proceedings of spie

Language(s) - English

Resource type - Conference proceedings

SCImago Journal Rank - 0.192

H-Index - 176

eISSN - 1996-756X

pISSN - 0277-786X

DOI - 10.1117/12.824873

Subject(s) - wavelet , multivariate random variable , random field , basis (linear algebra) , legendre wavelet , covariant transformation , multiresolution analysis , mathematics , wavelet transform , computer science , pattern recognition (psychology) , pure mathematics , artificial intelligence , algorithm , algebra over a field , discrete wavelet transform , random variable , statistics , geometry

This paper is concerned with the mathematical characterization and wavelet analysis of self-similar random vector fields. The study consists of two main parts: the construction of random vector models on the basis of their invariance under coordinate transformations, and a study of the consequences of conducting a wavelet analysis of such random models. In the latter part, after briefly examining the effects of standard wavelets on the proposed random fields, we go on to introduce a new family of Laplacian-like vector wavelets that in a way duplicate the covariant-structure and whitening relations governing our random models.

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