Figure-Ground Segmentation Using Multiple Cues

The theme of this thesis is figure-ground segmentation. We address the problem in the context of a visual observer, e.g. a mobile robot, moving around in the world and capable of shifting its gaze to and fixating on objects in its environment. We are only considering bottom-up processes, how the system can detect and segment out objects because they stand out from their immediate background in some feature dimension. Since that implies that the distinguishing cues can not be predicted, but depend on the scene, the system must rely on multiple cues. The integrated use of multiple cues forms a major theme of the thesis. In particular, we note that an observer in our real environment has access to 3-D cues. Inspired by psychophysical findings about human vision we try to demonstrate their effectiveness in figure-ground segmentation and grouping also in machine vision

Real-Time Maintenance of Figure-Ground Segmentation

An approach to figure-ground segmentation based on a a 2-dimensional histogram in feature space is presented. The histogram is then analyzed with a peak-finding algorithm designed with realtime performance in mind. The most significant peaks in the histogram are backprojected to the image to produce an object mask