Ground from Figure Discrimination

Abstract

This paper proposes a new, efficient, figure from ground discrimination method. This algorithm is based on the assumption that background data features can be more easily detected than figure data features, thus emphasizing the background detection task (and implying the name of the method). Along the iterative labeling process, data features are sequentially and permanently labeled as “background,” while more global information is being collected to assist with the coming decisions, until the process converges. This procedure creates a bootstrap mechanism which improves performance in very cluttered scenes. The method can be applied to many perceptual grouping cues, and an application to smoothness-based classification of edge points is given. A fast implementation using a kd-tree allows one to work on large, realistic images.