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Original author(s)Andrew Kirillov
Initial releaseDecember 21, 2006; 14 years ago (2006-12-21)[1]
Stable release
2.2.5 / July 17, 2013; 7 years ago (2013-07-17)
Written inC#
Operating systemCross-platform
LicenseLGPLv3 and partly GPLv3

AForge.NET is a computer vision and artificial intelligence library originally developed by Andrew Kirillov for the .NET Framework.[2]

The source code and binaries of the project are available under the terms of the Lesser GPL and the GPL (GNU General Public License).[citation needed]

Another (unaffiliated) project called Accord.NET was created to extend the features of the original AForge.NET library.[3]

Discontinuation of free public support and future development

On April 1, 2012, Andrew Kirillov announced the end of the public support for the library, temporarily closing the discussion forums. The last release of the AForge.NET Framework was made available on July 17, 2013. However, since its release 3.0 in 2015, the Accord.NET project started to incorporate most of the original AForge.NET source code in its codebase, continuing its support and development under the Accord.NET name.[3]


The framework's API includes support for:

  • Computer vision, image processing and video processing[4]
    • Including a comprehensive image filter library
  • Artificial Neural networks library implements some common network architectures (multi-layer feed forward and distance networks) and learning algorithms (back propagation, delta rule, simple perceptron, evolutionary learning).
  • Genetic algorithms, genetic programming and gene expression programming
  • Fuzzy logic
  • Machine learning
  • and libraries for a select set of robotics kits
    • Lego Mindstorms NXT and RCX kits

The framework is provided not only with different libraries and their sources, but with many sample applications, which demonstrate the use of this framework, and with documentation help files, which are provided in HTML Help format. A number of software applications[5][6][7][8] and research works[9][10][11] utilized the framework.

See also

  • List of free and open source software packages
  • List of numerical libraries for .NET framework
  • Accord.NET - Computer vision and artificial intelligence library that extends AForge.NET.
  • OpenCV - A popular C++ computer vision library.
  • VXL - Another C++ computer vision library.
  • CVIPtools - A complete GUI based computer vision and image processing software environment.
  • OpenNN - An open source C++ neural networks library.


  1. ^ "AForge.NET :: AForge.NET Framework celebrates its 5 years birthday".
  2. ^ Greg Duncan. Portable Image and Video processing with help from AForge.NET and Accord.NET. [1] Channel 9, November 2014. Web extract
  3. ^ a b Souza, César (20 May 2010). "Accord.NET Framework – An extension to AForge.NET". Retrieved 2018-11-26.
  4. ^ Peter Shaw (3 June 2015). "Computer Vision Using AForge.NET". .NET Nuts & Bolts. Code Guru. Archived from the original on 2018-11-26. Retrieved 2018-11-26.
  5. ^ Andrew Kirilov. "Projects and applications using AForge.NET Framework". AForge.NET. Archived from the original on 2018-11-26. Retrieved 2018-11-26.
  6. ^ "Universe Sandbox". Archived from the original on 2018-11-26. Retrieved 2018-11-26.
  7. ^ "NeurApp, Exploring Approximation by Artificial Neural Networks". Archived from the original on 2018-11-26. Retrieved 2018-11-26.
  8. ^ "iSpy: Open Source Video Surveillance Software". Retrieved 2018-11-26.
  9. ^ S M Hassan Ahmed; Todd C Alexander; Georgios Anagnostopoulos (May 2015). "Real-time, Static and Dynamic Hand Gesture Recognition for Human-Computer Interaction". University of Miami. Retrieved 2018-11-26.
  10. ^ Suraj Verma, Prashant Pillai, Yim-Fun Hu (2012). "Development of an eye-tracking control system using AForge.NET framework". International Journal of Intelligent Systems Technologies and Applications. 11 (3/4): 286. doi:10.1504/IJISTA.2012.052485. Archived from the original on 2018-11-26. Retrieved 2018-11-26.CS1 maint: multiple names: authors list (link)
  11. ^ A. Meena; K. Raja (2012). K-Means Segmentation of Alzheimer's Disease in Pet Scan Datasets – An Implementation. International Joint Conference on Advances in Signal Processing and Information Technology. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. 117. pp. 168–172. arXiv:. doi:10.1007/978-3-319-11629-7_24. ISBN 978-3-319-11628-0.

External links

Edited: 2021-06-18 19:49:07