Self-Motion and Depth Estimation from Image Sequences
An image-based version of a computational model of human self-motion perception (developed in collaboration with Dr. Leland S. Stone at NASA Ames Research Center) has been generated and tested. The research included in the grant proposal sought to extend the utility of the self-motion model so that it could be used for explaining and predicting human performance in a greater variety of aerospace applications. The model can now be tested with video input sequences (including computer generated imagery) which enables simulation of human self-motion estimation in a variety of applied settings.
Perrone, John↗