The framework comes with over 25 sample applications demonstrating the framework features. All sample applications are written in C#.

Contents 1 Audio 1.1 Fourier 1.2 Beat detector 1.3 Recorder 2 Imaging 2.1 Harris Corners Detector 2.2 Panorama (image stitching) 2.3 Wavelets 3 Machine Learning 3.1 Handwriting (Multi-class SVM) 3.2 RANdom SAmple Consensus (RANSAC) 3.3 Kernel Support Vector Machines 3.4 K-Means Color Clustering 3.5 Gaussian Mixture Models 3.6 Decision Trees 4 Math 4.1 Matrix Operations 4.2 Quadratic Programming Solver 5 Neuro 5.1 Levenberg-Marquardt 5.1.1 Approximation 5.1.2 Time Series 5.1.3 Exclusive-OR (XOR) Problem 6 Statistics 6.1 Handwriting (KDA) 6.2 Hidden Markov Models 6.3 Kernel Discriminant Analysis 6.4 Kernel Principal Component Analysis 6.5 Linear Discriminant Analysis 6.6 Principal Component Analysis 6.7 Independent Component Analysis 6.8 Partial Least Squares 6.9 Logistic Regression 6.10 Receiver Operating Characteristic (ROC) Curves 6.11 Mouse Gestures Recognition 7 Vision 7.1 Face Detection (Viola-Jones) 7.2 Face Tracking (Camshift) 7.3 Head-based Controller

Audio

Samples relative to the Accord.Audio namespace. The Audio namespace is still experimental, so the applications may still be incomplete or may require further optimizations.

Fourier

A simple audio spectrum analyzer using Fast Fourier Transform (FFT). Can optionally use audio windows (i.e. Hamming) to reduce bin leakage in the spectrum.

Beat detector

A simple beat detector which listens to the default input device and tries to detect peaks in the audio signal. It is a statistics based beat detector in the sense it searches local energy peaks which may contain a beat. The application also demonstrates the usage of the Metronome class of the framework, which allows one to detect the current tempo by "tapping" the metronome.

Currently, only the default sound input is supported.

Recorder

A simple wave recorder able to capture sound from the microphone.

Imaging

Samples relative to the Accord.Imaging namespace.

Harris Corners Detector

Demonstration of the Harris corners detector in the world-famous Lena Söderberg's picture.

Panorama (image stitching)

The Panorama sample application demonstrates how the framework can be used to automatically stitches two images together by using the Harris corners detector, Correlation matching, homography estimation, RANSAC and the image blending filter.

Wavelets

Demonstration of the Haar and CDF9/7 wavelet transform for images.

Machine Learning

Samples relative to the Accord.MachineLearning namespace.

Handwriting (Multi-class SVM)

Handwritten digits recognition by using Multi-class Kernel Support Vector Machines.

RANdom SAmple Consensus (RANSAC)

The RANSAC sample application demonstrates how to use RANSAC to robustly fit a linear regression, avoiding the negative impact of outliers in the regression data.

Kernel Support Vector Machines

A sample application demonstrating how to perform classification and regression using (Kernel) Support Vector Machines. The sample datasets which can be used in the application can be found in the Resources folder in the application main directory.

K-Means Color Clustering

A sample application demonstrating the use of K-Means for color reduction (color clustering) in images.

Gaussian Mixture Models

Multivariate Gaussian mixture distribution fitting using Expectation-Maximization. The method is first initialized using K-Means clustering.

Decision Trees

Decision tree learning with ID3 and C4.5 algorithms.

Math

Samples relative to the Accord.Math namespace.

Matrix Operations

Demonstrates how to perform simple matrix operations using Accord.NET Matrix library.

Quadratic Programming Solver

Quadratic Programming (QP) problem solving using the dual method of Goldfarb and Idnani. Translated from the original Fortran code by Berwin A. Turlach.

Neuro

Samples relative to the Accord.Neuro namespace.

Levenberg-Marquardt

An adaptation of the original AForge.NET Neuro sample applications to work with Levenberg-Marquardt instead of Backpropagation.

Approximation

Function approximation using Neural Networks trained by the Levenberg-Marquardt learning algorithm.

Time Series

Time series prediction using Neural Networks trained by the Levenberg-Marquardt learning algorithm.

Exclusive-OR (XOR) Problem

Solves the XOR classification problem using Neural Networks trained by the Levenberg-Marquardt learning algorithm.

Statistics

Samples relative to the Accord.Statistics namespace.

Handwriting (KDA)

Handwritten digits recognition by using Non-linear (Multiple) Discriminant Analysis using Kernels (KDA).

Hidden Markov Models

Demonstrates how to use Hidden Markov Models (HMMs) and Accord.NET Markov Sequence Classifiers to recognize sequences of discrete observations.

Kernel Discriminant Analysis

Sample application demonstrating how to use Kernel Discriminant Analysis (also known as KDA, or Non-linear (Multiple) Discriminant Analysis using Kernels) to perform non-linear transformation and classification. The sample datasets which can be used in the application are available under the Resources folder in the main directory of the application.

Kernel Principal Component Analysis

Sample application demonstrating how to use Kernel Principal Component Analysis (KPCA) to perform non-linear transformations and dimensionality reduction. The sample datasets which can be used in the application are available under the Resources folder in the main directory of the application.

Linear Discriminant Analysis

Sample application demonstrating how to use Linear Discriminant Analysis (also known as LDA, or Fisher's (Multiple) Linear Discriminant Analysis) to perform linear transformations and classification. The sample datasets which can be used in the application are available under the Resources folder in the main directory of the application.

Principal Component Analysis

Sample application demonstrating how to use Principal Component Analysis (PCA) to perform linear transformations and dimensionality reduction. The sample datasets which can be used in the application are available under the Resources folder in the main directory of the application.

Independent Component Analysis

Sample application demonstrating how to use Independent Component Analysis (ICA) to perform blind source separation of audio signals. The audio is processed using the Accord.Audio modules of the framework.

Partial Least Squares

Demonstrates how to use Partial Least Squares to fit a (multiple and multivariate) linear regression model from high-dimensionality data.

Logistic Regression

Sample application for creating and fitting Logistic Regression models. Also fits a multiple linear regression model for comparison purposes, and performs chi-square tests and computes Wald's statistics for the logistic regression coefficients.

Receiver Operating Characteristic (ROC) Curves

Sample application demonstrating how to create and visualize Receiver-Operating Characteristic Curves from a given set of results from a test or a classification process.

Mouse Gestures Recognition

Learning and recognition of mouse gestures using Continuous density Hidden Markov Models.

Vision

Samples relative to the Accord.Vision namespace.

Face Detection (Viola-Jones)

Face detection using the Face detection based in Haar-like rectangular features method popularly known as the Viola-Jones method.

Face Tracking (Camshift)

Face (or object) tracking using Viola-Jones for face detection and Camshift as the object tracker. Can be used in RGB and HSL color spaces (may require some tuning for HSL).

Head-based Controller

Sample application demonstrating how to use the Accord.Vision.Controls.Controller component to provide joystick-like controls for a Windows Form application. Its component design makes adding support for head-based controlling as easy as dragging and dropping a component into a Form.