Multilevel statistical shape models: A new framework for modeling hierarchical structures

Statistical shape models are commonly used in various applications of computer vision. Nevertheless, these models are not well adapted to hierarchical structures. This paper proposes a solution to this problem by presenting a general framework to build multilevel statistical shape models. Based on multilevel component analysis, the idea is to decompose the data into a within-individual and a between-individual component. As a result, several sub-models are deduced and can be treated separately, each level characterizing one sub-model. In this paper, we present a multilevel model of the human spine. The results show that such a modelization offers more flexibility and allows deformations that classical statistical models can simply not generate.