Software extracts key content from images
Advancements in imaging technologies are producing vast amounts of data that can overwhelm analysts. A team of engineers, computer scientists, and physicists at Lawrence Livermore Laboratory (Livermore, CA, USA; www.llnl.gov) has developed a new extraction system, the Image Content Engine, which allows analysts to search volumes of data in a timely manner by guiding them to areas in the images that likely contain the objects for which they are searching.
Advancements in imaging technologies are producing vast amounts of data that can overwhelm human analysts. A team of engineers, computer scientists, and physicists at Lawrence Livermore National Laboratory (LLNL; Livermore, CA, USA; www.llnl.gov) has developed a new extraction system, the Image Content Engine (ICE), which allows analysts to search massive volumes of data in a timely manner by guiding them to areas in the images that likely contain the objects for which they are searching.
The ICE can accommodate images acquired with different types of overhead, including airborne, sensors and at varying resolutions. The software can account for the fact that images are taken at different times of the day, during different seasons, and under changing weather conditions. "Finding specific objects, such as particular types of buildings or vehicles, in overhead images that cover hundreds of square kilometers, is a difficult task," says engineer David Paglieroni, technical lead and coprincipal investigator of ICE.
The ICE architecture can run on different computing platforms and operating systems, such as Windows or Linux, laptops or powerful clusters of computers, and isolated or networked processors. The ICE software contains a library of algorithms, each of which focuses on a specific task. The algorithms can be chained together in pipelines configured through a graphical user interface. Each pipeline is designed to perform a specific set of tasks for extracting specific image content.
The ICE also provides tools for extracting regions, extended curves, and polygons from images. The region-extraction tool breaks images into small, adjacent square tiles containing one or more pixels. For each tile, the algorithm searches for spectral or textural characteristics and then groups tiles with similar features into regions. This tool is useful for separating distinct areas such as forests, bodies of water, plowed fields, and clusters of buildings from the image background.
The ICE was developed under a three-year Laboratory Directed Research and Development Strategic Initiative begun in 2003. It encompasses a new approach for the computer-aided extraction of specific content information from different kinds of images, but especially those images taken with overhead sensors.