Efficient Generation of Synthetic Terrain Imagery for Automatic Target Recognition
Abstract
We have developed an integrated framework for the efficient synthesis of realistic images of three dimensional terrain with man-made objects and natural clutter. We have shown how many phenomenological aspects of the placement of road networks, tree clusters, populated areas, buildings, etc., can be captured in a computationally-efficient pseudorandom image-generation framework. We generate the underlying terrain with a fast Fourier based procedure. This procedure is seeded with a pseudorandom sequence, so a small number of parameters can characterize a distinctive class of terrain. Within each class, arbitrarily many scenes can be synthesized. We have used procedural definitions for objects; targets or clutter are represented by different models depending on range. For example, at a great distance trees may look like simple cones. But, as the viewpoint nears a tree, finer details will become visible. Procedural definitions also eliminate the need to explicitly store boundary representations for every object placed in the scene. A single procedural definition for an object, such as a tree, can be used at render time by a Z-buffer algorithm to create as many instances as are necessary, with each instance being unique since it uses a unique pseudorandom seed.... Automatic target recognition, Image synthesis, Computer modeling, Z-buffer.
Document Details
- Document Type
- Technical Report
- Publication Date
- Oct 23, 1992
- Accession Number
- ADA260705
Entities
People
- A. C. Kak
- Huicong Kang
- L. L. Grewe
- R. L. Cromwell
- S. G. Blask
Organizations
- Purdue University