Image pixel guided tours: a software platform for non-destructive x-ray imaging

Abstract

Multivariate analysis seeks to describe the relationship between an arbitrary number of variables. To explore highdimensional data sets, projections are often used for data visualisation to aid discovering structure or patterns that lead to the formation of statistical hypothesis. The basic concept necessitates a systematic search for lower-dimensional representations of the data that might show interesting structure(s). Motivated by the recent research on the Image Grand Tour (IGT), which can be adapted to view guided projections by using objective indexes that are capable of revealing latent structures of the data, this paper presents a signal processing perspective on constructing such indexes under the unifying exploratory frameworks of Independent Component Analysis (ICA) and Projection Pursuit (PP). Our investigation begins with an overview of dimension reduction techniques by means of orthogonal transforms, including the classical procedure of Principal Component Analysis (PCA), and extends to an application of the more powerful techniques of ICA in the context of our recent work on non-destructive testing technology by element specific x-ray imaging.