An output-sensitive algorithm for the minimization of 2-dimensional String Covers

String covers are a powerful tool for analyzing the quasi-periodicity of 1-dimensional data and find applications in automata theory, computational biology, coding and the analysis of transactional data. A \emph{cover} of a string $T$ is a string $C$ for which every letter of $T$ lies within some occurrence of $C$. String covers have been generalized in many ways, leading to \emph{k-covers}, \emph{$\lambda$-covers}, \emph{approximate covers} and were studied in different contexts such as \emph{indeterminate strings}. In this paper we generalize string covers to the context of 2-dimensional data, such as images. We show how they can be used for the extraction of textures from images and identification of primitive cells in lattice data. This has interesting applications in image compression, procedural terrain generation and crystallography

Movement Expressivity Analysis: From Theory to Computation

Movement expressivity, on which we focus in this chapter, has been widely studied and described by psychologists, sociologists, and neuroscientists. More recently, movement expressivity is receiving increasing attention also by computer scientists with the aim to develop machines with social and emotional intelligence. This chapter, after providing a definition of expressive movement, describes qualitative and quantitative methods, frameworks and algorithms for movement expressivity analysis