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Multivariate locally stationary 2D wavelet processes with application to colour texture analysis

Research output: Contribution to journalJournal article

Published
<mark>Journal publication date</mark>07/2017
<mark>Journal</mark>Statistics and Computing
Issue number4
Volume27
Number of pages15
Pages (from-to)1129-1143
Publication statusPublished
Early online date1/07/16
Original languageEnglish

Abstract

In this article we propose a novel framework for the modelling of non-stationary multivariate lattice processes. Our approach extends the locally stationary wavelet paradigm into the multivariate two-dimensional setting. As such the framework we develop permits the estimation of a spatially localised spectrum within a channel of interest and, more importantly, a localised cross-covariance which describes the localised coherence between channels. Associated estimation theory is also established which demonstrates that this multivariate spatial framework is properly defined and has suitable convergence properties. We also demonstrate how this model-based approach can be successfully used to classify a range of colour textures provided by an industrial collaborator, yielding superior results when compared against current state-of-the-art statistical image processing methods.