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Locally stationary wavelet fields with application to the modelling and analysis of image texture.

Research output: Contribution to journalJournal article


<mark>Journal publication date</mark>08/2010
<mark>Journal</mark>Journal of the Royal Statistical Society: Series C (Applied Statistics)
Number of pages22
<mark>Original language</mark>English


This article proposes the modelling and analysis of image texture using an extension of a locally stationary wavelet process model into two-dimensions for lattice processes. Such a model permits construction of estimates of a spatially localized spectrum and localized autocovariance which can be used to characterize texture in a multiscale and spatially adaptive way. We provide the necessary theoretical support to show that our two-dimensional extension is properly defined and has the proper statistical convergence properties. Our use of a statistical model permits us to identify, and correct for, a bias in established texture measures based on non-decimated wavelet techniques. The proposed method performs nearly as well as optimal Fourier techniques on stationary textures and outperforms them in non-stationary situations. We illustrate our techniques using pilled fabric data from a fabric care experiment and simulated tile data.