Hanson, A. (1983). Overview of the Image Understanding Testbed. SRI INTERNATIONAL MENLO PARK CA.
The Image Understanding Testbed is a system of hardware and software that is designed to facilitate the integration, testing, and evaluation of implemented research concepts in machine vision. The system was developed by the Artificial Intelligence Center of SRI International under the joint sponsorship of the Defense Advanced Research Projects Agency (DARPA) and the Defense Mapping Agency (DMA). The primary purpose of the Image Understanding (IU) Testbed is to provide a means for transferring technology from the DARPA-sponsored IU research program to DMA and other organizations in the defense community. The approach taken to achieve this purpose has two components:
- The establishment of a uniform environment that will be as compatible as possible with the environments of research centers at universities participating in the IU program. Thus, organizations obtaining copies of the testbed can receive new results of ongoing research as they become available.
- The acquisition, integration, testing, and evaluation of selected scene analysis techniques that represent mature examples of generic areas of research activity. These contributions from IU program participants will allow organizations with testbed copies to immediately begin investigating potential applications of IU technology to problems in automated cartography and other areas of scene analysis.
An important component of the DARPA IU research program is the development of image-understanding techniques that could be applied to automated cartography and military image interpretation tasks; this work forms the principal focus of the testbed project. A number of computer modules developed by participants in the IU program have been transported to the uniform testbed environment as a first step in the technology transfer process. These include systems written in UNIX C, MAINSAIL, and FRANZ LISP. Capabilities of the computer programs include segmentation, linear feature delineation, shape detection, stereo reconstruction, and rule-based recognition of classes of three-dimensional objects.