  In this thesis a new approach for touch detection on optical multi-touch
devices is proposed that exploits the fact that the camera images reveal not
only the actual touch points but also objects above the screen such as the hand
or arm of a user. The touch processing relies on the Maximally Stable Extremal
Regions algorithm for finding the users' fingertips in the camera image. The
hierarchical structure of the generated extremal regions serves as a starting
point for agglomerative clustering of the fingertips into hands. Furthermore, a
heuristic is suggested that supports the identification of individual fingers
as well as the distinction between left hands and right hands if all five
fingers of a hand are in contact with the touch surface.
  The evaluation confirmed that the system is robust against detection errors
resulting from non-uniform illumination and reliably assigns touch points to
individual hands based on the implicitly tracked context information. The
efficient multi-threaded implementation handles two-handed input from multiple
users in real-time.
