IM2Learn


Repository: https://opensource.ncsa.illinois.edu/stash/scm/image/im2learn.git
Documentation: PDF
Bug Reporting: Jira
License: NCSA Open Source
Status: Supported

The Im2Learn (Image to Learn) suite of tools is designed to address the gap between complex multi-instrument raw data and knowledge. Im2Learn automates repetitive analysis tasks and builds user-friendly decision-making systems for a variety of applications, including 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, bioinformatics, microscopy and medical image processing, and advanced sensor environments.The motivation for developing 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. The objective of the Im2Learn suite of tools is to provide 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,
  • bio-informatics,
  • microscopy and medical image processing, and
  • advanced sensor environments.

Im2Learn provides code for the automated processing of repetitive, laborious and tedious analysis tasks and user-friendly decision-making systems that operate in automated or semi-automated modes in a variety of applications. The software is based on methods taken from computer vision, data fusion, statistical and spectral modeling.

Source code is available from our git repository at https://opensource.ncsa.illinois.edu/stash/scm/image/im2learn.git and can be checked out as follows:

git clone https://opensource.ncsa.illinois.edu/stash/scm/image/im2learn.git

If you wish to contribute code to the project please contact kooper@illinois.edu

Algae predicted values at water stations.
Soil Drainage Map Buena Vista Basis
Results of pattern analysis on Buena Vista Basin
Illinois elevation in pseudocolor representation.
Selected Illinois Waterstations
Map of Buena Vista Basin
map and quality overlay
Overlay of the elevation map of Illinois and the Algae predicted values.
The initial image and its corresponding mask are loaded into the Im2Learn Image Calculator. The resulting segmented image may be used to analyze the digital color of the specified area in alternate color spaces.
Digital images were obtained from the rich corpus collected by the Online Froissart project. All of the digital surrogates analyzed for this project were captured using consistently applied image capture and handling protocols.
The face of the figure
will be the point of analysis and eventual comparison.
GEOLearn Flow
Elevation map of Illinois with rivers and water resources.