Staam: Fitting a 2D+3D AAM to Stereo Images

This paper proposes a stereo active appearance model fitting algorithm (STAAM), that fits a 2D+3D active appearance model to stereo images acquired from calibrated perspective cameras. The STAAM uses geometrical relationship between cameras to compute the 3D shape and rigid motion parameters. The use of calibration information reduces the number of model parameters, restricts the degree of freedom in the model parameters, and increases the accuracy and speed of fitting. Also, the STAAM uses a modified simultaneous update fitting method that reduces the fitting computation greatly. Experimental results show that (2) the STAAM shows a better fitting stability than the existing multi-view AAM, (2) the modified simultaneous update algorithm accelerates the AAM fitting speed.