The motivation for developing Image to Learn (Im2Learn) comes from academic, government and industrial collaborations that involve development of new computer methods and solutions for understanding complex data sets. Images and other types of data generated by various instruments and sensors form complex and highly heterogeneous data sets, and pose challenges on knowledge extraction.
In general, the driver for the Im2Learn suite of tools is to address the gap between complex multi-instrument raw data and knowledge relevant to any specific application. The objective of the Im2Learn suite of tools is to research and develop solutions to real life problems in the application areas of:
- machine vision,
- precision farming,
- land use and land cover classification,
- map analysis,
- geo-spatial information systems (GIS),
- synthetic aperture radar (SAR) target and multi-spectral scene modeling,
- video surveillance,
- microscopy and medical image processing, and
- advanced sensor environments.
The main goal of the Im2Learn research and development is to automate information processing of repetitive, laborious and tedious analysis tasks and build user-friendly decision-making systems that operate in automated or semi-automated mode in a variety of applications. The development is based on theoretical foundations of image and video processing, computer vision, data fusion, statistical and spectral modeling
Image to Learn was created at the National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign. We would like to acknowledge multiple funding agencies for the support including NSF, NIH, NASA, NARA, ONR, DARPA, NAVY and NCSA Industrial Partners.
The main creators of Im2Learn are Rob Kooper, David Clutter, Sang-Chul Lee and Peter Bajcsy with the help from graduate and undergraduate students, namely Tenzing Shaw, Wei-Wen Feng, and Peter Ferak.