
Audio beat detector

Spectrum analyzer (Fourier)

Wave Recorder

Robot (Inference)

Sets (Fuzzy, Linguistic)

Genetic Programming

Traveling Salesman (GP)

Classification (BagofWords and SVM)

Corners detection (FAST)

Corners detection (Harris)

Corners detection (SURF)

Image filters

Blobs detection

Shape detection

Image stitching (FREAK)

Image stitching (Harris)

Image stitching (SURF)

Hough Transform

Image Viewer

Pose Estimation (POSIT)

Texture

Wavelets

Animat

Gestures (Dynamic Time Warp SVMs)

RANdom SAmple Consensus (RANSAC)

Handwriting (Multiclass SVM)

Classification (Kernel SVMs)

Classification (Naive Bayes)

Classification (Decision Trees)

Regression (Kernel SVM)

Clustering (KMeans and MeanShift)

Clustering (Gaussian Mixture Models)

Liblinear (Linear SVMs)

Feature Selection (L1regularized Logistic SVMs)

Matrices

Pose Coordinates (POSIT)

Quadratic Programming (QP) Solver

DenavitHartenberg Kinematics

Classification (LevenbergMarquardt)

Deep Belief Networks and Boltzmann Machines

LevenbergMarquardt

Perceptron

Resilient Backpropagation (RProp)

SelfOrganizing Maps (SOM)

Traveling Salesman (SOM)

Handwriting recognition (KDA)

Filters

Hidden Markov Models

Kernel Discriminant Analysis (KDA)

Kernel Principal Component Analysis (KPCA)

Linear Discriminant Analysis (LDA)

Principal Component Analysis (PCA)

Independent Component Analysis (ICA)

Partial Least Squares (PLS)

Linear and Logistic Regression Analysis

Multinomial Logistic Regression Analysis

Eigenfaces (PCA)

Receiver Operating Characteristic (ROC) Curves

Cox\'s Proportional Hazards

Mouse Gesture Recognition

Face Detection (Haar object detector)

Face Tracking (Camshift)

Headbased Controller

Color glove segmentation and tracking

Dynamic Virtual Wall

Kinect Capture (v1)

Snapshot Maker

Two Cameras

Video Player

Ximea Sample

Screencast Capture Lite
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A simple beat detector that listens to an input device and tries to detect peaks in the audio signal. It is a statisticsbased beat detector in the sense it searches local energy peaks which may contain a beat.
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The Fourier sample application shows how to capture sounds from a capture device (such as a microphone jack) using the Accord.NET Framework. The signal can be analyzed, processed and transformed using the framework's Fourier and Hilbert transform functions.
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The Wave Recorder sample application demonstrates how to use the IAudioOutput and IAudioSource interfaces to capture and output sound. This is just a sample application, however. The intent of the framework is not to allow building of audio players, but to support the use of audio signals in machine learning and statistics experiments.
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Shows how to perform image classification using the BagofVisualWords (BoW) model with SURF features and the Binary Split algorithm.
The BoW model is used to transform the many SURF feature points in a image in a single, fixedlength feature vector. The feature vector is then used to train Support Vector Machines (SVMs) using a variety of kernels.
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The FAST sample application demonstrates how to perform corners detection using the FAST corners detector. As the name implies, the FAST detector is one of the fastest corners detectors available.
_{Corner point detection with the FAST algorithm.}
The current code is based on the excellent Edward Rosten's implementation, and is dual licensed under the permissive BSD license.
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The Harris sample application demonstrates how to perform corners detection using the Harris algorithm.
_{Corner point detection with the Harris algorithm.}
The current implementation supports both Harris and Nobel corner measures. Harris can be enabled by checking the checkbox next to the ''k'' parameter, which is only needed for Harris. The Nobel measure does not require setting any parameters.
A suitable choice for the threshold parameter when using Harris measure may range around 10,000 to 30,000, while when using Nobel it may range around 20 to 100. Best sigma values are usually higher than 0.3 and lesser than 5.0.
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The SURF sample application shows how to use the SpeededUp Robust Features (SURF) Detector.
_{Interest point detection and description with the SURF algorithm.}
The current implementation is based on the excellent OpenSURF library by Christopher Evans. The framework version, however, comes with further performance optimizations.
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Demonstrates how to perform automatic image stitching by interest point matching. The actual stitching uses many parts of the framework, such as the FREAK feature detector, RANSAC, knearest neighbor matching, homography estimation and linear gradient image blending.
_{Image stitching using the FREAK feature detection and extraction algorithm.}
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Demonstrates how to perform automatic image stitching by interest point matching. The actual stitching uses many parts of the framework, such as the Harris corners detector, RANSAC, correlation window matching, homography estimation and linear gradient image blending.
_{Image stitching using Harris corners detection and correlation matching.}
For more details about the method, please be sure to read the accompanying article on how to perform automatic image stitching by interest point matching in C#.
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Demonstrates how to perform automatic image stitching by interest point matching. The actual stitching uses many parts of the framework, such as the SURF feature detector, RANSAC, knearest neighbor matching, homography estimation and linear gradient image blending.
_{Image stitching using SURF features and knearest neighbor matching.}
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The Wavelet sample application shows how to use the Wavelet transform filter to process images using wavelet transforms such as the Haar and CDF9/7.
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This sample application shows how to use the Random Sample Consensus (RANSAC) algorithm to fit linear regression models. The algorithm works with any model or function, producing a robust version of the model which is less sensitive to outliers. Here is a detailed explanation on how RANSAC works.
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This sample application shows how to teach Multiclass Support Vector Machines using Sequential Minimal Optimization to recognize handwritten digits from the UCI's Optdigits dataset.
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This sample application shows how to use Kernel Support Vector Machines (kSVMs) to solve a classification problem. The sample application comes with default sample data with can be loaded in the File > Open menu.
After a sample data has been loaded, one can configure the settings and create a learning machine in the second tab. The picture below shows the decision surface for the YingYang classification data generated by a heuristically initialized Gaussiankernel SVM after it has been trained using Sequential Minimal Optimization (SMO). The framework offers an extensive list of kernel functions to choose from.
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This application shows how to use Naive Bayes for binary classification tasks.
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The Decision Trees sample application demonstrates how to create and use Decision Trees in C#. Decision trees are simple predictive models which map input attributes to a target value using simple conditional rules. Trees are commonly used in problems whose solutions must be readily understandable or explainable by humans, such as in computeraided diagnostics and credit analysis.
Here is a detailed explanation on Decision Trees.
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Function regression using (Kernel) Support Vector Machines.
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This sample application shows how to use the KMeans clustering algorithm and the mean shift clustering algorithm to perform color clustering, reducing the number of distinct colors in a given image.
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This sample application shows how to use Gaussian Mixture Models to perform clustering and classification using softdecision margins.
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This sample application shows how to recreate the liblinear.exe command line application using the SVM algorithms provided by the framework. The framework can perform almost all liblinear algorithms in C#, except for one. Those include:
0 — L2regularized logistic regression (primal)
1 – L2regularized L2loss support vector classification (dual)
2 — L2regularized L2loss support vector classification (primal)
3 — L2regularized L1loss support vector classification (dual)
4 –
5 — L1regularized L2loss support vector classification
6 — L1regularized logistic regression
7 — L2regularized logistic regression (dual) for regression
11 — L2regularized L2loss support vector regression (primal)
12 — L2regularized L2loss support vector regression (dual)
13 — L2regularized L1loss support vector regression (dual)
The framework can perform also load to and from files stored in LibSVM's sparse format. This means it should be straightforward to create or learn your models using one tool and run it on the other, if that would be necessary. For example, given that Accord.NET can run on mobile applications, it is possible to create and learn your models in a computing grid using liblinear and then integrate it in your Windows Phone application by loading it in Accord.NET.
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Feature selection using sparse L1regularized logistic support vector machines. This sample application shows how to create special linear SVMs with logistic functions to perform feature selection.
_{A problem that can be perfectly separated using only X.}
_{The machine accurately says that X is the most important feature.}
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Quadratic Programming (QP) problem solving using the dual method of Goldfarb and Idnani. Translated from the original Fortran code by Berwin A. Turlach.
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Demonstrates how to use DenavitHartenberg parameters and equations to model kinematic chains.
_{DenavitHartenberg forward kinematics sample application.}
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This sample application shows how to use the LevenbergMarquardt learning algorithm together with Bayesian regularization to teach a feedforward neural network.
_{Data classification with Neural Networks using the LevenbergMarquardt algorithm with and without Bayesian regularization.}
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This sample application shows how to learn Deep Neural Networks using Restricted Boltzmann Machines and the ContrastiveDivergence algorithm.
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An adaptation of the original AForge.NET Neuro sample applications to work with LevenbergMarquardt instead of Backpropagation. Includes solutions for approximation, timeseries prediction and the exclusiveor (XOR) problem using neural networks trained by LevenbergMarquardt. For more information regarding the method of LevenbergMarquardt, please take a look on Neural Network Learning by the LevebergMarquardt Algorithm with Bayesian Regularization.
_{LevenbergMarquardt algorithm for Approximation, Time Series, and the XOR problems.}
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An adaptation of the original AForge.NET Neuro sample applications to work with Resilient Backpropagation instead of Backpropagation. Includes solutions for approximation, timeseries prediction and the exclusiveor (XOR) problem using neural networks trained by LevenbergMarquardt.
_{RProp algorithm for Approximation, Time Series, and the XOR problems.}
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This sample application shows how to perform handwritten digit recognition using Kernel Discriminant Analysis and Linear Discriminant Analysis.
_{Handwritten digits with KDA sample application}
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Demonstrates how to use Hidden Markov Models (HMMs) and Accord.NET Markov Sequence Classifiers to recognize sequences of discrete observations.
_{Hidden Markov Model sample application}
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Sample application demonstrating how to use Kernel Discriminant Analysis (also known as KDA, or Nonlinear (Multiple) Discriminant Analysis using Kernels) to perform nonlinear 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 discriminant analysis}
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Sample application demonstrating how to use Kernel Principal Component Analysis (KPCA) to perform nonlinear 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.
_{Kernel principal component analysis}
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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
_{Linear discriminant analysis}
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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
_{Principal component analysis}
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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.
_{Independent component analysis for blind source separation}
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Demonstrates how to use Partial Least Squares to fit a (multiple and multivariate) linear regression model from highdimensionality data.
_{Partial least squares analysis}
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Sample application for creating and fitting Logistic Regression models. Also fits a multiple linear regression model for comparison purposes, and performs chisquare tests and computes Wald's statistics for the logistic regression coefficients.
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Sample application demonstrating how to classify objects into different classes using Multinomial Logistic Regression Analsysis. Performs chisquare tests and computes Wald's statistics for the logistic regression coefficients.
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Sample application demonstrating how to use Principal Component Analysis (PCA) to perform image classification. The application implements the Eigenfaces technique to distinguish between depth images of human hands in different shapes.
_{Principal component analysis}
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Sample application demonstrating how to create and visualize ReceiverOperating Characteristic Curves from a given set of results from a test or a classification process.
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How to perform survival analysis using Cox's Proportional Hazards model for both regression and prediction of timecensured data.
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Learning and recognition of mouse gestures using hidden Markov modelbased classifiers and Hidden Conditional Random Fields. Showcased in a series of CodeProject articles under the name Sequence Classifiers in C#.
_{Mouse gesture recognition with hidden Markov models.}
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Face detection using the Face detection based in Haarlike rectangular features method often known as the ViolaJones method
_{Haar cascade face detection.}
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Face (or object) tracking using ViolaJones for face detection and Camshift as the object tracker. Can be used in RGB and HSL color spaces (may require some tuning for HSL)
_{Face tracking with the Camshift algorithm.}
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Sample application demonstrating how to use the Accord.Vision.Controls.Controller component to provide joysticklike controls for a Windows Form application. Its component design makes adding support for headbased controlling as easy as dragging and dropping a component into a Form.
_{Controlling a computer interface using computer vision.}
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How to segment and track color objects using a simple and inexpensive HSLbased color tracker.
_{Colorbased video tracking.}
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Screencast Capture Lite is a tool for recording the desktop screen and saving it to a video file, preserving quality as much as possible. It is a real application, and also a demonstration of the use of the AForge.NET and Accord.NET Frameworks to build multimedia applications, capturing video and audio streams from different sources.