Many Vision-Based Human-Computer Interaction (VB-HCI) systems are based on the tracking of user actions. Examples include
gaze-tracking, head-tracking, finger-tracking, and so forth. In this paper, we present a framework that employs no user-tracking;
instead, all interface components continuously observe and react to changes within a local image neighborhood. More specifically,
components expect a pre-defined sequence of visual events called Visual Interface Cues (VICs). VICs include color, texture,
motion and geometric elements, arranged to maximize the veridicality of the resulting interface element. A component is
executed when this stream of cues has been satisfied.
We present a general architecture for an interface system operating under the VIC-Based HCI paradigm, and then focus specifically
on an appearance-based system in which a Hidden Markov Model (HMM) is employed to learn the gesture dynamics. Our implementation
of the system successfully recognizes a button-push with a 96% success rate. The system operates at frame-rate on standard
PCs.