Accord.NET Framework – An extension to AForge.NET

Today I have just released the first version of Accord.NET. The Accord.NET Framework is a C# framework I have developed over the years while working on several areas of artificial intelligence, machine learning and statistics.

The Accord.NET Framework extends the excellent AForge.NET Framework with new tools and libraries. In order to use Accord.NET, you must have AForge.NET already installed. The first version number of Accord.NET will be used to indicate the compatibility status with AForge.NET versions, thus the first version will be starting at 2.0.0.


The framework is comprised by the set of libraries and sample applications, which demonstrate their features:

  • Accord.Statistics – library with statistical analysis and other tools;
  • Accord.Imaging – extension to the AForge.NET Imaging library with new filters and routines;
  • Accord.Neuro – extension to the AForge.NET Neuro library with other learning algorithms;
  • Accord.MachineLearning – extension to AForge’s machine learning library with Support Vector Machines;
  • Accord.Audio – experimental library with filters and audio processing routines.


The Framework has just been released, so be ready to expect bugs and unpolished/unfinished areas. The code is released under a LGPL license. For additional help, issues, and discussions, please refer to the recently created forums.

Harris Corners Detector in C#

Corner detection, or the more general terminology interest point detection, is an approach used within computer vision systems to extract certain kinds of features from an image. Corner detection is frequently used in motion detection, image matching, tracking, image mosaicing, panorama stitching, 3D modelling and object recognition.

This code has also been incorporated in Accord.NET Framework, which includes the latest version of this code plus many other statistics and machine learning tools. In order to install the Accord.NET Framework in your projects, use NuGet. Type in the package manager: Install-Package Accord.Imaging


One of the firsts operators for interest point detection was developed by Hans P. Moravec in 1977 for his research involving the automatic navigation of a robot through a clustered environment. It was also Moravec who defined the concept of “points of interest” in a image and concluded these interest points could be used to find matching regions in different images.

The Moravec operator is considered to be a corner detector because it defines interest points as points where there are large intensity variations in all directions. This often is the case at corners. It is interesting to note, however, that Moravec was not specifically interested in finding corners, just distinct regions in an image that could be used to register consecutive image frames.

The Harris Operator

Harris Corners detected on the famous Lenna image.

This operator was developed by Chris Harris and Mike Stephens in 1988 as a processing step to build interpretations of a robot’s environment based on image sequences. Like Moravec, they needed a method to match corresponding points in consecutive image frames, but were interested in tracking both corners and edges between frames.

Harris and Stephens improved upon Moravec’s corner detector by considering the differential of the corner score with respect to direction directly. The Harris corner detector computes the locally averaged moment matrix computed from the image gradients, and then combines the Eigenvalues of the moment matrix to compute a corner measure, from which maximum values indicate corners positions.

Source Code

Below is the source code for the Harris Corners Detector algorithm. This is mostly the same code implemented in the Accord.NET Framework.

Using the code

Code usage is very simple. A Corners Marker filter from the AForge Framework can be used to directly draw the detected interest points in the original image as it would be usual within the framework.

Sample application

The accompanying sample application is pretty much self-explanatory. It performs the corners detection in the famous Lena Söderberg‘s picture, but can be adapted to work with other pictures as well. Just add another image to the project settings’ resources and change the line of code which loads the bitmap.

It is also very straightforward to adapt the application to load arbitrary images from the hard disk. I have opted to leave it this way for simplicity.



Before using any information, applications or source code available in this article, please be sure to have read the site usage disclaimer.

Recognition of Handwritten Digits using Non-linear Kernel Discriminant Analysis


I’ve just submitted a new article to CodeProject, entitled "Handwriting Recognition using Kernel Discriminant Analysis". The article is a demonstration of handwritten digit recognition using Non-linear Discriminant Analysis with Kernels and using the Optical Recognition of Handwritten Digits data set from the University of California’s Machine Learning Repository.

Recognition of handwritten digits using Non-linear Kernel Discriminant Analysis

Recognition of Handwritten digits using Kernel Discriminant Analysis


The Code Project is a free repository of source code, tutorials and development resources. It is also home of a large and ever-growing community of software developers, architects and other technology professionals. It has fairly active forums, and is a reasonably good resource for resolving difficult software development issues.