Ground from figure discrimination

Abstract

This paper proposes a new, efficient, figure from ground method. At every stage the data features are classified to either 'background' or 'unknown yet' classes, thus emphasizing the background detection task (and implying the name of the method). The sequential application of such classification stages creates a bootstrap mechanism which improves performance in very cluttered scenes. This 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 to work on large, realistic images.