Shape representation from image sequences by using binary statistical morphology

A real-time visual surveillance system is based on three main image processing phases, devoted to extract information about the observed scene: change detection, focus of attention, feature-extraction. In this paper attention is paid to a theory (i.e., binary statistical morphology) which provides a common framework for designing fast and noise-robust methods for the three tasks of interest. The main theoretical novelty is to establish a link between binary statistical morphology and voting methods. An application is presented which deals with intruder detection in a railway-crossing area.