Portal de Periódicos da CAPES

Dead Leaves Models: From Space Tessellations to Random Functions

2021; Springer Nature; Linguagem: Inglês

10.1007/978-3-030-75452-5_11

2196-9973

Dominique Jeulin,

Cultural Heritage Materials Analysis

Sequential models with support in \( {\mathbb{R}}^{n} \) are developed. For each point x in \( {\mathbb{R}}^{n} \), the models combine families of independent random sets or random functions, indexed by a parameter t. By a masking process, the Dead Leaves models (and its generalized version, the Markovian jumps sequential RF) simulate random images with objects in the foreground partially masking objects in the background, as seen in perspective views. The main probabilistic properties of the models are presented for the following cases: Dead Leaves tessellation, Color Dead Leaves, Dead Leaves RF, multivariate Dead Leaves RF and varieties, Markov Jumps RF. It is illustrated by applications to the morphological characterization of powders.