Edge Detection and Shape Recognition in Neutron Transmission Images

Neutron transmission measurements are a valuable tool for nondestructively imaging special nuclear materials. Analysis of these images, however, tends to require significant user interaction to determine the sizes, shapes, and likely compositions of measured objects. Computer vision (CV) techniques can be a useful approach to automatically extracting important information from either neutron transmission images or fission-site-mapping images. An automatable approach has been developed that processes an input image and, through recursive application of CV techniques, produces a set of basic shapes that define surfaces observed in the image. These shapes can then be compared to a library of known shape configurations to determine if the measured object matches its expected configuration, as could be done behind an information barrier for arms control treaty verification inspections.