Accord.NET Extensions is an extension framework for Accord.NET and AForge.NET. The framework sets focus on .NET native array as primary imaging object and offers computer-vision algorithms mostly build as extensions. The libraries can be grouped as following:

Image processing

• Accord.Extensions.Imaging.Algorithms package
  Implements image processing algorithms as .NET array extensions including the Accord.NET algorithms.

• Accord.Extensions.Imaging.Algorithms.LINE2D package
  Implements template matching algorithm (~20x faster than conventional sliding window approach).

  Tutorials: Fast template matching

• Accord.Extensions.Vision package
  Pyramidal KLT tracker, Camshift, Meanshift

  Tutorials: Object Feature Tracking

Math libraries

• Accord.Extensions.Math package
  Fluent matrix extensions. Geometry and graph structures and extensions.

• Accord.Extensions.Statistics package
  Object tracking algorithms: Kalman filter, particle filter, Joint Probability Data Association Filter (JPDAF).
  

  Tutorials: Kalman filter, Particle filter

Support libraries

• Accord.Extensions.Imaging.AForgeInterop package
  Interoperability extensions between .NET array and AForge's UnmanagedImage.

• Accord.Extensions.Core package
  Element caching, parallel extensions, shared structures.

Getting started

Browse through some included samples, install NuGet packages and enjoy!
Take a look at:

• DotImaging - a portable imaging library - the foundation of Accord.NET Extensions
• MoreCollections - a set of portable collections installable as source files via NuGet.

How to Engage, Contribute and Provide Feedback

The best ways to contribute and provide feedback is to try things out, file bugs and propose enhancements. Your opinion is important and will define the future roadmap. If you have any questions, comments or you would like to propose an enhancement please leave the message on Github, or write to: darko.juric2 [at] gmail.com.

Final word

If you like the project please star it in order to help to spread the word. That way you will make the framework more significant and in the same time you will motivate me to improve it, so the benefit is mutual.