Paper
4 September 2009 Self-similar random vector fields and their wavelet analysis
Author Affiliations +
Abstract
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.
© (2009) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Pouya Dehghani Tafti and Michael Unser "Self-similar random vector fields and their wavelet analysis", Proc. SPIE 7446, Wavelets XIII, 74460Y (4 September 2009); https://doi.org/10.1117/12.824873
Lens.org Logo
CITATIONS
Cited by 3 scholarly publications.
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Wavelets

Mathematical modeling

Fourier transforms

Stochastic processes

Data modeling

Motion analysis

Fractal analysis

RELATED CONTENT

Simulation of geometric Brownian motion in stock price
Proceedings of SPIE (April 22 2022)
Wavelet analysis of DNA sequences
Proceedings of SPIE (September 01 1995)
Multiscale analysis of well and seismic data
Proceedings of SPIE (October 01 1998)
Multi-scale representations of the motion trajectory
Proceedings of SPIE (November 15 2007)

Back to Top