Randomness analysis of images of aggregates

The specific "randomness" of images of aggregates needs a better understanding, mainly for classification purposes. Such images can be modeled by morphological tools (random set theory) with certain limitations. This paper tries to find a different path by using classical and less classical linear tools and comparing them, in a quantitative or qualitative way. Singular value decomposition plays an important role, as a decorrelation tool, and as a generator for the spectrum of singular values. The "log-profile" of this spectrum is central in this study; in the context of random matrix theory, its properties (approximate linearity...) permit to refer to "pure random situations"; thus, images of aggregates can be understood and modeled as perturbation to these situations. Moreover, independent components analysis corroborates in many cases the preceding results.