This paper introduces the first ever appearance-based simulator of burst illumination laser sequences from a single, conventional video image of a vehicle. The appearance-based approach allows us to dispose of the very complex physical models needed to achieve realism. The system uses a dictionary of 3-D, geometric object models and a dictionary of intensity-time profile examples. The latter were obtained from real images acquired for a number of different materials and surface orientations. To generate a synthetic time-gated sequence the user provides simply a single, conventional photograph of a vehicle in a desired orientation. The photograph is matched interactively to a database of 3-D geometric models of vehicles, estimating 3-D pose and approximate relative depths at all points. Depths are then used to simulate time-gating, and consequently to decide which object parts are imaged for every depth. Model surface orientation and material are used to index the example dictionary and assign an intensity-time profile to imaged pixels. Results indicate very promising performance.

An important challenge of any skin color based tracking system is to accommodate varying illumination conditions. We present a method for automatic transformation of the color planes to match the skin color model learnt for a fixed illumination. The first couple of initial frames are used to automatically extract the palm region which in turn serves as an observed skin color palette which needs to be color-transformed to match a similar palette under a known illumination. A neural network implementing back-propagation learning rule then performs the color correction for the entire sequence. We use a condensation algorithm for tracking after the color correction for improved performance.