Scalable singular 3D modeling for digital battlefield applications

We propose a new classification algorithm to detect and classify targets of interest. It is based on an advanced brand of analytic geometry of manifolds, called theory of catastrophes. Physical Optics Corporation's (POC) scalable 3D model representation provides automatic and real-time analysis of a discrete frame of sensed 2D imagery of terrain, urban, and target features. It then transforms this frame of discrete different-perspective 2D views of a target into a 3D continuous model called a pictogram. The unique local stereopsis feature of this modeling is the surprising ability to locally obtain a 3D pictogram from a single monoscopic photograph. The proposed 3D modeling, combined with more standard change detection algorithms and 3D terrain feature models, will constitute a novel classification algorithm and a new type of digital battlefield imagery for Imaging Systems.