Detecting and Ranking Foreground Regions in Gray-Level Images

Abstract

Starting from a gray-level image partitioned into regions by watershed segmentation, we introduce a method to assign the regions to the foreground and the background, respectively. The method is inspired by visual perception and identifies the border between foreground and background in correspondence with the locally maximal changes in gray-level. The obtained image representation is hierarchical, both due to the articulation of the assignment process into three steps, aimed at the identification of components of the foreground with decreasing perceptual relevance, and due to a parameter taking into account the distance of each foreground region from the most relevant part in the same foreground component. Foreground components are detected by resorting to both global and local processes. Global assignment, cheaper from a computational point of view, is accomplished as far as this can be safely done. Local assignment takes place in the presence of conflictual decisions.