samples.html

---
layout: default
title: "Sample gallery"
comments: true
---

<div>See <a href="https://github.com/accord-net/framework/wiki/Sample-applications">sample applications at wiki page</a>.</div>

<div id="samples">

    <ul class="filter nav nav-pills">
        <li data-value="all"><a href="#">All</a></li>
        <!-- BEGIN CATEGORIES -->
            <li data-value="audio"><a href="#">Audio</a></li>
            <li data-value="fuzzy"><a href="#">Fuzzy</a></li>
            <li data-value="genetics"><a href="#">Genetics</a></li>
            <li data-value="imaging"><a href="#">Imaging</a></li>
            <li data-value="machine-learning"><a href="#">Machine Learning</a></li>
            <li data-value="mathematics"><a href="#">Mathematics</a></li>
            <li data-value="neuro"><a href="#">Neuro</a></li>
            <li data-value="statistics"><a href="#">Statistics</a></li>
            <li data-value="unity"><a href="#">Unity</a></li>
            <li data-value="video"><a href="#">Video</a></li>
            <li data-value="vision"><a href="#">Vision</a></li>
<!-- END CATEGORIES -->
    </ul>

    <hr>

    <ul class="thumbnails">
        <!-- BEGIN THUMBNAILS -->

            <li data-type="audio" data-id="id-0" class="sample-app">
              <a href="#" id="thumb-0"><h2>Audio beat detector</h2><img src="https://camo.githubusercontent.com/7db35b7f446233dc49453f800713419f75833a46/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d617564696f2d74656d706f6465746563746f722e706e67" alt="Audio beat detector"></a>
            </li>

            <li data-type="audio" data-id="id-1" class="sample-app">
              <a href="#" id="thumb-1"><h2>Spectrum analyzer (Fourier)</h2><img src="https://camo.githubusercontent.com/af6ec77a807dee3352de6d404b5720a42b52f4a6/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d617564696f2d666f75726965722e706e67" alt="Spectrum analyzer (Fourier)"></a>
            </li>

            <li data-type="audio" data-id="id-2" class="sample-app">
              <a href="#" id="thumb-2"><h2>Wave Recorder</h2><img src="https://camo.githubusercontent.com/c622efbb2ad73bba7084209fda162e6e89c032a2/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d617564696f2d7265636f7264696e672e706e67" alt="Wave Recorder"></a>
            </li>

            <li data-type="fuzzy" data-id="id-3" class="sample-app">
              <a href="#" id="thumb-3"><h2>Robot (Inference)</h2><img src="https://camo.githubusercontent.com/f9beedc49a7a4bd086e1977f99894d407bb459cd/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d66757a7a792d696e666572656e63652e706e67" alt="Robot (Inference)"></a>
            </li>

            <li data-type="fuzzy" data-id="id-4" class="sample-app">
              <a href="#" id="thumb-4"><h2>Sets (Fuzzy, Linguistic)</h2><img src="https://camo.githubusercontent.com/dff3c187819b80f2c4a69b01de707dfbdea3e82c/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d66757a7a792d736574732e706e67" alt="Sets (Fuzzy, Linguistic)"></a>
            </li>

            <li data-type="genetics" data-id="id-5" class="sample-app">
              <a href="#" id="thumb-5"><h2>Genetic Programming</h2><img src="https://camo.githubusercontent.com/468ab2db1ad02d04e6ff541c7f064f0076fc1257/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d67656e657469632d70726f6772616d6d696e672e706e67" alt="Genetic Programming"></a>
            </li>

            <li data-type="genetics" data-id="id-6" class="sample-app">
              <a href="#" id="thumb-6"><h2>Traveling Salesman (GP)</h2><img src="https://camo.githubusercontent.com/15a9cf6ef3113847467267cb549dc46626c2ca58/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d67656e657469632d74726176656c696e672d73616c65736d616e2e706e67" alt="Traveling Salesman (GP)"></a>
            </li>

            <li data-type="imaging" data-id="id-7" class="sample-app">
              <a href="#" id="thumb-7"><h2>Classification (Bag-of-Words and SVM)</h2><img src="https://camo.githubusercontent.com/4f6a0ec344b35c69f6f565479cfda3f2acee989d/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d696d6167696e672d636c617373696669636174696f6e2e706e67" alt="Classification (Bag-of-Words and SVM)"></a>
            </li>

            <li data-type="imaging" data-id="id-8" class="sample-app">
              <a href="#" id="thumb-8"><h2>Corners detection (FAST)</h2><img src="https://camo.githubusercontent.com/29f2a61a53089916211f52c70f3d44f82a71c4e1/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d696d6167696e672d666173742e706e67" alt="Corners detection (FAST)"></a>
            </li>

            <li data-type="imaging" data-id="id-9" class="sample-app">
              <a href="#" id="thumb-9"><h2>Corners detection (Harris)</h2><img src="https://camo.githubusercontent.com/920975495009fba4f9e18db8b64d012b6aa99844/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d696d6167696e672d6861727269732e706e67" alt="Corners detection (Harris)"></a>
            </li>

            <li data-type="imaging" data-id="id-10" class="sample-app">
              <a href="#" id="thumb-10"><h2>Corners detection (SURF)</h2><img src="https://camo.githubusercontent.com/556d6010123ba7c627328cf537bd213ef95ba0fc/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d696d6167696e672d737572662e706e67" alt="Corners detection (SURF)"></a>
            </li>

            <li data-type="imaging" data-id="id-11" class="sample-app">
              <a href="#" id="thumb-11"><h2>Blobs detection</h2><img src="https://camo.githubusercontent.com/33936d6e9d960fc583ea474e98d24a4f25b01dac/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d696d6167696e672d626c6f62732e706e67" alt="Blobs detection"></a>
            </li>

            <li data-type="imaging" data-id="id-12" class="sample-app">
              <a href="#" id="thumb-12"><h2>Shape detection</h2><img src="https://camo.githubusercontent.com/31061091707952d6271e2dd56a0686f69f70e90c/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d696d6167696e672d7368617065732e706e67" alt="Shape detection"></a>
            </li>

            <li data-type="imaging" data-id="id-13" class="sample-app">
              <a href="#" id="thumb-13"><h2>Image filters</h2><img src="https://camo.githubusercontent.com/e3dbc784a9e23d9c0aa01feb97eff6464b611dcb/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d696d6167696e672d66696c746572732e706e67" alt="Image filters"></a>
            </li>

            <li data-type="imaging" data-id="id-14" class="sample-app">
              <a href="#" id="thumb-14"><h2>Hough Transform</h2><img src="https://camo.githubusercontent.com/023ea4e4029950f03287eff34d45893fd7d68ecf/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d696d6167696e672d686f7567682e706e67" alt="Hough Transform"></a>
            </li>

            <li data-type="imaging" data-id="id-15" class="sample-app">
              <a href="#" id="thumb-15"><h2>Image Viewer</h2><img src="https://camo.githubusercontent.com/145e8faa629c4065c0de62b7ddf80b206d3621ec/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d696d6167696e672d696d6167652d7669657765722e706e67" alt="Image Viewer"></a>
            </li>

            <li data-type="imaging" data-id="id-16" class="sample-app">
              <a href="#" id="thumb-16"><h2>Image stitching (FREAK)</h2><img src="https://camo.githubusercontent.com/e7dc85e4865af86ccf0463305eee0bb10022acc7/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d696d6167696e672d70616e6f72616d612d667265616b2e706e67" alt="Image stitching (FREAK)"></a>
            </li>

            <li data-type="imaging" data-id="id-17" class="sample-app">
              <a href="#" id="thumb-17"><h2>Image stitching (Harris)</h2><img src="https://camo.githubusercontent.com/7dbd9533ca0eb95bba248cdfd2584fe9210ce406/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d696d6167696e672d70616e6f72616d612d6861727269732e706e67" alt="Image stitching (Harris)"></a>
            </li>

            <li data-type="imaging" data-id="id-18" class="sample-app">
              <a href="#" id="thumb-18"><h2>Image stitching (SURF)</h2><img src="https://camo.githubusercontent.com/48d7480845d3e64641e858a0bc2d81b0b63e9bd8/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d696d6167696e672d70616e6f72616d612d737572662e706e67" alt="Image stitching (SURF)"></a>
            </li>

            <li data-type="imaging" data-id="id-19" class="sample-app">
              <a href="#" id="thumb-19"><h2>Pose Estimation (POSIT)</h2><img src="https://camo.githubusercontent.com/fdc438b46a3ba1705c09f19c18d0d5c4a379a348/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d696d6167696e672d706f73652d657374696d6174696f6e2d706f7369742e706e67" alt="Pose Estimation (POSIT)"></a>
            </li>

            <li data-type="imaging" data-id="id-20" class="sample-app">
              <a href="#" id="thumb-20"><h2>Texture</h2><img src="https://camo.githubusercontent.com/3fd943ec49287f699bf652398699c552b297e3b3/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d696d6167696e672d74657874757265732e706e67" alt="Texture"></a>
            </li>

            <li data-type="imaging" data-id="id-21" class="sample-app">
              <a href="#" id="thumb-21"><h2>Wavelets</h2><img src="https://camo.githubusercontent.com/6437a6e36f4db44276e3ba5503bb684263deaa35/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d696d6167696e672d776176656c6574732e706e67" alt="Wavelets"></a>
            </li>

            <li data-type="machine-learning" data-id="id-22" class="sample-app">
              <a href="#" id="thumb-22"><h2>Animat (QLearning and Sarsa)</h2><img src="https://camo.githubusercontent.com/823e3e55ce6f3f52887e0bbfcc607b20759781f8/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d6d616368696e656c6561726e696e672d616e696d61742e706e67" alt="Animat (QLearning and Sarsa)"></a>
            </li>

            <li data-type="machine-learning" data-id="id-23" class="sample-app">
              <a href="#" id="thumb-23"><h2>Classification (Kernel SVMs)</h2><img src="https://camo.githubusercontent.com/d673e59f5a94860ff8b7dbbad6440b6be32d0f14/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d6d616368696e656c6561726e696e672d636c617373696669636174696f6e2d73766d2d312e706e67" alt="Classification (Kernel SVMs)"></a>
            </li>

            <li data-type="machine-learning" data-id="id-24" class="sample-app">
              <a href="#" id="thumb-24"><h2>Clustering (Gaussian Mixture Models)</h2><img src="https://camo.githubusercontent.com/2bd6339dfbe653f320ce9ea8c4ec44e673a38274/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d6d616368696e656c6561726e696e672d636c7573746572696e672d676d6d2e706e67" alt="Clustering (Gaussian Mixture Models)"></a>
            </li>

            <li data-type="machine-learning" data-id="id-25" class="sample-app">
              <a href="#" id="thumb-25"><h2>Clustering (K-Means and MeanShift for color clustering)</h2><img src="https://camo.githubusercontent.com/866948db5c92ec4b19da82b83207cf7e154085a7/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d6d616368696e656c6561726e696e672d636c7573746572696e672d6d65616e73686966742d6b6d65616e732e706e67" alt="Clustering (K-Means and MeanShift for color clustering)"></a>
            </li>

            <li data-type="machine-learning" data-id="id-26" class="sample-app">
              <a href="#" id="thumb-26"><h2>Clustering 2D (K-Means, Mean-Shift, GMM)</h2><img src="https://camo.githubusercontent.com/8000632da1b1e6a0e39445cc6eaba8dc84f1e917/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d6d616368696e656c6561726e696e672d636c7573746572696e672d32642e706e67" alt="Clustering 2D (K-Means, Mean-Shift, GMM)"></a>
            </li>

            <li data-type="machine-learning" data-id="id-27" class="sample-app">
              <a href="#" id="thumb-27"><h2>Classification (Decision Trees)</h2><img src="https://camo.githubusercontent.com/808a3bb92d2c93e846d187a0a67a6be3a3f1884c/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d6d616368696e656c6561726e696e672d636c617373696669636174696f6e2d74726565732e706e67" alt="Classification (Decision Trees)"></a>
            </li>

            <li data-type="machine-learning" data-id="id-28" class="sample-app">
              <a href="#" id="thumb-28"><h2>Feature Selection (L1-regularized Logistic SVMs)</h2><img src="https://camo.githubusercontent.com/c22fe34eb7e9347857e6632e54e9eb8177ee40c7/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d6d616368696e656c6561726e696e672d666561747572652d73656c656374696f6e2e706e67" alt="Feature Selection (L1-regularized Logistic SVMs)"></a>
            </li>

            <li data-type="machine-learning" data-id="id-29" class="sample-app">
              <a href="#" id="thumb-29"><h2>RANdom SAmple Consensus (RANSAC)</h2><img src="https://camo.githubusercontent.com/00795c3ae7001c3829cab7b4ff642408024405c8/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d6d616368696e656c6561726e696e672d72616e7361632e706e67" alt="RANdom SAmple Consensus (RANSAC)"></a>
            </li>

            <li data-type="machine-learning" data-id="id-30" class="sample-app">
              <a href="#" id="thumb-30"><h2>Gestures (Dynamic Time Warping SVMs)</h2><img src="https://camo.githubusercontent.com/83a326b3cc6595758e85a5e561448f3610d0dfe4/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d6d616368696e656c6561726e696e672d67657374757265732d73766d2e706e67" alt="Gestures (Dynamic Time Warping SVMs)"></a>
            </li>

            <li data-type="machine-learning" data-id="id-31" class="sample-app">
              <a href="#" id="thumb-31"><h2>Handwriting (Multi-class SVM)</h2><img src="https://camo.githubusercontent.com/3162ebcc0cbb19db226122fe2541353f41c27fe1/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d6d616368696e656c6561726e696e672d68616e6477726974696e672d73766d2e706e67" alt="Handwriting (Multi-class SVM)"></a>
            </li>

            <li data-type="machine-learning" data-id="id-32" class="sample-app">
              <a href="#" id="thumb-32"><h2>Liblinear (Linear SVMs)</h2><img src="https://camo.githubusercontent.com/d59a564dffe8c2b64468b08fdc78cf4595f5c3f6/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d6d616368696e656c6561726e696e672d6c69626c696e6561722e706e67" alt="Liblinear (Linear SVMs)"></a>
            </li>

            <li data-type="machine-learning" data-id="id-33" class="sample-app">
              <a href="#" id="thumb-33"><h2>Classification (Naive Bayes)</h2><img src="https://camo.githubusercontent.com/fff00e1c8c45b924f24e5598aadf7f6e0e21af9f/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d6d616368696e656c6561726e696e672d636c617373696669636174696f6e2d6e62617965732e706e67" alt="Classification (Naive Bayes)"></a>
            </li>

            <li data-type="machine-learning" data-id="id-34" class="sample-app">
              <a href="#" id="thumb-34"><h2>Regression (Kernel SVM)</h2><img src="https://camo.githubusercontent.com/1dada516366e675364e79b286e47a2474728a7d4/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d6d616368696e656c6561726e696e672d72656772657373696f6e2d73766d2e706e67" alt="Regression (Kernel SVM)"></a>
            </li>

            <li data-type="mathematics" data-id="id-35" class="sample-app">
              <a href="#" id="thumb-35"><h2>Matrices</h2><img src="https://camo.githubusercontent.com/2ab2d9a35f7a1fed9f525767ad04b97dac28061a/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d6d6174682d6d61747269786f7065726174696f6e732d696d672e706e67" alt="Matrices"></a>
            </li>

            <li data-type="mathematics" data-id="id-36" class="sample-app">
              <a href="#" id="thumb-36"><h2>Pose Coordinates (POSIT)</h2><img src="https://camo.githubusercontent.com/97defd469ef0d2a61d07fc3ff28d2e26f74b5adc/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d6d6174682d706f73652d657374696d6174696f6e2d706f7369742e706e67" alt="Pose Coordinates (POSIT)"></a>
            </li>

            <li data-type="mathematics" data-id="id-37" class="sample-app">
              <a href="#" id="thumb-37"><h2>Quadratic Programming / Goldfarb-Idnani (QP) Solver</h2><img src="https://camo.githubusercontent.com/9d68882bb620991667f6b93a0dfae9a4ba0ebb85/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d6d6174682d736f6c7665722d28676f6c64666172622d69646e616e69292e706e67" alt="Quadratic Programming / Goldfarb-Idnani (QP) Solver"></a>
            </li>

            <li data-type="mathematics" data-id="id-38" class="sample-app">
              <a href="#" id="thumb-38"><h2>Denavit-Hartenberg Kinematics</h2><img src="https://camo.githubusercontent.com/608e3f860ff60ee4fa340b8e01fd57922a0a882a/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d6d6174682d64656e617669742e706e67" alt="Denavit-Hartenberg Kinematics"></a>
            </li>

            <li data-type="neuro" data-id="id-39" class="sample-app">
              <a href="#" id="thumb-39"><h2>Classification (Levenberg-Marquardt)</h2><img src="https://camo.githubusercontent.com/db0a7de503753de02185d274cb0a31c3338d3336/687474703a2f2f696d672e796f75747562652e636f6d2f76692f77486c686d50376e4e396f2f302e6a7067" alt="Classification (Levenberg-Marquardt)"></a>
            </li>

            <li data-type="neuro" data-id="id-40" class="sample-app">
              <a href="#" id="thumb-40"><h2>Deep Belief Networks and Boltzmann Machines</h2><img src="https://camo.githubusercontent.com/743ac11379c9d71336f7c723e76eab825c11c0e5/687474703a2f2f696d672e796f75747562652e636f6d2f76692f6d37644138366f794a76552f302e6a7067" alt="Deep Belief Networks and Boltzmann Machines"></a>
            </li>

            <li data-type="neuro" data-id="id-41" class="sample-app">
              <a href="#" id="thumb-41"><h2>Levenberg-Marquardt</h2><img src="https://camo.githubusercontent.com/7ae12aa7323a1660eaffa9d4821beaa7cc14d761/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d6e6575726f2d6c6576656e626572672d6d61727175617264742e706e67" alt="Levenberg-Marquardt"></a>
            </li>

            <li data-type="neuro" data-id="id-42" class="sample-app">
              <a href="#" id="thumb-42"><h2>Perceptron</h2><img src="https://camo.githubusercontent.com/4326a50854c31c46b848df17bdc4393412894a27/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d6e6575726f2d70657263657074726f6e2e706e67" alt="Perceptron"></a>
            </li>

            <li data-type="neuro" data-id="id-43" class="sample-app">
              <a href="#" id="thumb-43"><h2>Resilient Backpropagation (RProp)</h2><img src="https://camo.githubusercontent.com/e2f22fc845402b9c6bb1f5f476890ecd2bbacf93/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d6e6575726f2d726573696c69656e742d6261636b70726f7061676174696f6e2e706e67" alt="Resilient Backpropagation (RProp)"></a>
            </li>

            <li data-type="neuro" data-id="id-44" class="sample-app">
              <a href="#" id="thumb-44"><h2>Self-Organizing Maps (SOM)</h2><img src="https://camo.githubusercontent.com/6a5ea96eefcdd575c0f2460bcffd4b733e166668/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d6e6575726f2d636c7573746572696e672d736f6d2e706e67" alt="Self-Organizing Maps (SOM)"></a>
            </li>

            <li data-type="neuro" data-id="id-45" class="sample-app">
              <a href="#" id="thumb-45"><h2>Traveling Salesman (SOM)</h2><img src="https://camo.githubusercontent.com/e7cc7c38069e85671267a10f6aebd9885bd3a77a/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d6e6575726f2d74726176656c6c696e672d73616c65736d616e2e706e67" alt="Traveling Salesman (SOM)"></a>
            </li>

            <li data-type="statistics" data-id="id-46" class="sample-app">
              <a href="#" id="thumb-46"><h2>Handwriting recognition (KDA)</h2><img src="https://camo.githubusercontent.com/183c9fdb2a053354fb07267aa3073686f8572e5e/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d737461746973746963732d68616e6477726974696e672d286b6461292d302e706e67" alt="Handwriting recognition (KDA)"></a>
            </li>

            <li data-type="statistics" data-id="id-47" class="sample-app">
              <a href="#" id="thumb-47"><h2>Filters (Tables)</h2><img src="https://camo.githubusercontent.com/e98549e7763b962148d144a824468a36c51f04cb/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d737461746973746963732d66696c746572732e706e67" alt="Filters (Tables)"></a>
            </li>

            <li data-type="statistics" data-id="id-48" class="sample-app">
              <a href="#" id="thumb-48"><h2>Hidden Markov Models</h2><img src="https://camo.githubusercontent.com/c7b356fa72070aba484234d28ebc076daeba91f9/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d737461746973746963732d73657175656e6365732d28686d6d292e706e67" alt="Hidden Markov Models"></a>
            </li>

            <li data-type="statistics" data-id="id-49" class="sample-app">
              <a href="#" id="thumb-49"><h2>Kernel Discriminant Analysis (KDA)</h2><img src="https://camo.githubusercontent.com/f2ccde0e7b912859bea9b8fafb51e8427817ce30/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d737461746973746963732d616e616c797369732d286b6461292e706e67" alt="Kernel Discriminant Analysis (KDA)"></a>
            </li>

            <li data-type="statistics" data-id="id-50" class="sample-app">
              <a href="#" id="thumb-50"><h2>Kernel Principal Component Analysis (KPCA)</h2><img src="https://camo.githubusercontent.com/cd4f4da29d7e78f8534bc5b02f32ab5c72689ec1/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d737461746973746963732d616e616c797369732d28706361292e706e67" alt="Kernel Principal Component Analysis (KPCA)"></a>
            </li>

            <li data-type="statistics" data-id="id-51" class="sample-app">
              <a href="#" id="thumb-51"><h2>Linear Discriminant Analysis (LDA)</h2><img src="https://camo.githubusercontent.com/0f7e9fb3b79f9702b6d50b3812637510b4102d7a/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d737461746973746963732d616e616c797369732d286c6461292e706e67" alt="Linear Discriminant Analysis (LDA)"></a>
            </li>

            <li data-type="statistics" data-id="id-52" class="sample-app">
              <a href="#" id="thumb-52"><h2>Principal Component Analysis (PCA)</h2><img src="https://camo.githubusercontent.com/cd4f4da29d7e78f8534bc5b02f32ab5c72689ec1/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d737461746973746963732d616e616c797369732d28706361292e706e67" alt="Principal Component Analysis (PCA)"></a>
            </li>

            <li data-type="statistics" data-id="id-53" class="sample-app">
              <a href="#" id="thumb-53"><h2>Independent Component Analysis (ICA)</h2><img src="https://camo.githubusercontent.com/241387e93c493271429a30a6189a955514d74e56/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d737461746973746963732d736f756e642d64656d6978696e672d28696361292e706e67" alt="Independent Component Analysis (ICA)"></a>
            </li>

            <li data-type="statistics" data-id="id-54" class="sample-app">
              <a href="#" id="thumb-54"><h2>Partial Least Squares (PLS)</h2><img src="https://camo.githubusercontent.com/ed2c22a0ed42de9486f450bba3ec40c63b51f111/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d737461746973746963732d616e616c797369732d28706c73292e706e67" alt="Partial Least Squares (PLS)"></a>
            </li>

            <li data-type="statistics" data-id="id-55" class="sample-app">
              <a href="#" id="thumb-55"><h2>Linear and Logistic Regression Analysis</h2><img src="https://camo.githubusercontent.com/547435e31134c5586275918386d32d1a5846b2d9/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d737461746973746963732d72656772657373696f6e2d286c696e6561722d616e642d6c6f676973746963292e706e67" alt="Linear and Logistic Regression Analysis"></a>
            </li>

            <li data-type="statistics" data-id="id-56" class="sample-app">
              <a href="#" id="thumb-56"><h2>Multinomial Logistic Regression Analysis</h2><img src="https://camo.githubusercontent.com/09ac77591c0f90018d802589e816181a5cd307ae/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d737461746973746963732d636c617373696669636174696f6e2d286d6c72292e706e67" alt="Multinomial Logistic Regression Analysis"></a>
            </li>

            <li data-type="statistics" data-id="id-57" class="sample-app">
              <a href="#" id="thumb-57"><h2>Eigenfaces (PCA)</h2><img src="https://camo.githubusercontent.com/811b18425687d62dfd54b7c6361b332aa925bf22/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d737461746973746963732d656967656e66616365732d28706361292e706e67" alt="Eigenfaces (PCA)"></a>
            </li>

            <li data-type="statistics" data-id="id-58" class="sample-app">
              <a href="#" id="thumb-58"><h2>Receiver Operating Characteristic (ROC) Curves</h2><img src="https://camo.githubusercontent.com/547435e31134c5586275918386d32d1a5846b2d9/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d737461746973746963732d72656772657373696f6e2d286c696e6561722d616e642d6c6f676973746963292e706e67" alt="Receiver Operating Characteristic (ROC) Curves"></a>
            </li>

            <li data-type="statistics" data-id="id-59" class="sample-app">
              <a href="#" id="thumb-59"><h2>Cox\'s Proportional Hazards</h2><img src="https://camo.githubusercontent.com/42fd7f345647cb93ef886f80a065d996ab35bf6f/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d737461746973746963732d737572766976616c2d28636f78292e706e67" alt="Cox\'s Proportional Hazards"></a>
            </li>

            <li data-type="statistics" data-id="id-60" class="sample-app">
              <a href="#" id="thumb-60"><h2>Mouse Gesture Recognition</h2><img src="https://camo.githubusercontent.com/1b826831f8aa1e136c97508fabcfb49ea4633b75/687474703a2f2f696d672e796f75747562652e636f6d2f76692f6a6e36726a4166334865512f302e6a7067" alt="Mouse Gesture Recognition"></a>
            </li>

            <li data-type="statistics" data-id="id-61" class="sample-app">
              <a href="#" id="thumb-61"><h2>Statistics Workbench (distributions)</h2><img src="https://camo.githubusercontent.com/2fc239c6309e1956eecf1545b7eecd179ae6a9b8/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d737461746973746963732d776f726b62656e63682e706e67" alt="Statistics Workbench (distributions)"></a>
            </li>

            <li data-type="unity" data-id="id-62" class="sample-app">
              <a href="#" id="thumb-62"><h2>Unity sample application</h2><img src="https://camo.githubusercontent.com/741b0b52b5a3b6aa0c0a82ca4a06da89ad336595/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d756e6974792d756e6974792e706e67" alt="Unity sample application"></a>
            </li>

            <li data-type="video" data-id="id-63" class="sample-app">
              <a href="#" id="thumb-63"><h2>Kinect Capture</h2><img src="https://camo.githubusercontent.com/37f2902232f4462aabbafd8ea9c1466e61f66f90/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d766973696f6e2d6b696e6563742d636170747572652e706e67" alt="Kinect Capture"></a>
            </li>

            <li data-type="video" data-id="id-64" class="sample-app">
              <a href="#" id="thumb-64"><h2>Snapshot Maker</h2><img src="https://camo.githubusercontent.com/9fb2d0a2f409e0a688203eb6deb479fec1107a10/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d766964656f2d736e617073686f742d6d616b65722e706e67" alt="Snapshot Maker"></a>
            </li>

            <li data-type="video" data-id="id-65" class="sample-app">
              <a href="#" id="thumb-65"><h2>Two Cameras</h2><img src="https://camo.githubusercontent.com/51dc8520e3c9e700b89c27bf7a88051a355d9e70/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d766964656f2d74776f2d63616d657261732e706e67" alt="Two Cameras"></a>
            </li>

            <li data-type="video" data-id="id-66" class="sample-app">
              <a href="#" id="thumb-66"><h2>Video Player</h2><img src="https://camo.githubusercontent.com/82e9e686f49cd2b1e3b1bb1c89bd86a5c2535ecf/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d766964656f2d766964656f2d706c617965722e706e67" alt="Video Player"></a>
            </li>

            <li data-type="video" data-id="id-67" class="sample-app">
              <a href="#" id="thumb-67"><h2>Ximea Sample</h2><img src="https://camo.githubusercontent.com/297af26d1198424399c69aedec84c5a60df379d0/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d766964656f2d78696d65612e706e67" alt="Ximea Sample"></a>
            </li>

            <li data-type="video" data-id="id-68" class="sample-app">
              <a href="#" id="thumb-68"><h2>Screencast Capture Lite</h2><img src="https://camo.githubusercontent.com/43f3f86238ea4f6a38fd214edb6ae6595dcff5de/687474703a2f2f696d672e796f75747562652e636f6d2f76692f6e5a70306e6758625f526f2f302e6a7067" alt="Screencast Capture Lite"></a>
            </li>

            <li data-type="vision" data-id="id-69" class="sample-app">
              <a href="#" id="thumb-69"><h2>Face Detection (Haar object detector)</h2><img src="https://camo.githubusercontent.com/da15f8332cc1f8a7f38b006f2f27a5a005160b03/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d766973696f6e2d666163652d646574656374696f6e2d2876696f6c612d6a6f6e6573292e706e67" alt="Face Detection (Haar object detector)"></a>
            </li>

            <li data-type="vision" data-id="id-70" class="sample-app">
              <a href="#" id="thumb-70"><h2>Face Tracking (Camshift)</h2><img src="https://camo.githubusercontent.com/bf8a8353959778cd06c6033953307e421994d5b4/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d766973696f6e2d666163652d747261636b696e672d2863616d7368696674292e706e67" alt="Face Tracking (Camshift)"></a>
            </li>

            <li data-type="vision" data-id="id-71" class="sample-app">
              <a href="#" id="thumb-71"><h2>Head-based Controller</h2><img src="https://camo.githubusercontent.com/f011c98eca35d019928b4166dd5a69ff18f99c94/687474703a2f2f696d672e796f75747562652e636f6d2f76692f74714166714a735732576f2f302e6a7067" alt="Head-based Controller"></a>
            </li>

            <li data-type="vision" data-id="id-72" class="sample-app">
              <a href="#" id="thumb-72"><h2>Color glove segmentation and tracking</h2><img src="https://camo.githubusercontent.com/bf8a8353959778cd06c6033953307e421994d5b4/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d766973696f6e2d666163652d747261636b696e672d2863616d7368696674292e706e67" alt="Color glove segmentation and tracking"></a>
            </li>

            <li data-type="vision" data-id="id-73" class="sample-app">
              <a href="#" id="thumb-73"><h2>Dynamic Virtual Wall</h2><img src="https://camo.githubusercontent.com/d4718e83c84fc0bea2d8e34a232c1e12a493c10d/68747470733a2f2f75706c6f61642e77696b696d656469612e6f72672f77696b6970656469612f636f6d6d6f6e732f7468756d622f362f36372f58626f782d3336302d4b696e6563742d5374616e64616c6f6e652e706e672f3132303070782d58626f782d3336302d4b696e6563742d5374616e64616c6f6e652e706e67" alt="Dynamic Virtual Wall"></a>
            </li>

            <li data-type="vision" data-id="id-74" class="sample-app">
              <a href="#" id="thumb-74"><h2>Motion Detector</h2><img src="https://camo.githubusercontent.com/622093784315f5ebf9597f322ef496f5e5aa4207/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d766973696f6e2d6d6f74696f6e2d6465746563746f722e706e67" alt="Motion Detector"></a>
            </li>
<!-- END THUMBNAILS -->
    </ul>

    <!-- BEGIN TEXT -->
            <div id="text-0" class="hidden">
<ul>
<li>Download the <a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLYKFq5bbVUucFUBiQ">application</a>
</li>
<li>Browse the <a href="https://github.com/accord-net/framework/tree/master/Samples/Audio/Beat%20detection">source code</a>
</li>
</ul>
<p>A simple beat detector that listens to an 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.</p>
<p><a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLYKFq5bbVUucFUBiQ"><img src="https://camo.githubusercontent.com/7db35b7f446233dc49453f800713419f75833a46/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d617564696f2d74656d706f6465746563746f722e706e67" alt="Audio beat detector and metronome sample application" data-canonical-src="http://accord-framework.net/images/samples/accord-audio-tempodetector.png"></a><br><sub>Audio beat detector and metronome.</sub></p>
</div>
            <div id="text-1" class="hidden">
<ul>
<li>Download the <a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLYMo_t0DVpY-1tOHw">application</a>
</li>
<li>Browse the <a href="https://github.com/accord-net/framework/tree/master/Samples/Audio/Fourier%20(FFT)">source code</a>
</li>
</ul>
<p>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.</p>
<p><a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLYMo_t0DVpY-1tOHw"><img src="https://camo.githubusercontent.com/af6ec77a807dee3352de6d404b5720a42b52f4a6/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d617564696f2d666f75726965722e706e67" alt="Fourier spectrum analyzer sample application" data-canonical-src="http://accord-framework.net/images/samples/accord-audio-fourier.png"></a><br><sub>Fourier spectrum analyzer sample application</sub></p>
</div>
            <div id="text-2" class="hidden">
<ul>
<li>Download the <a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLYNxBPIMWUIoi2NoA">application</a>
</li>
<li>Browse the <a href="https://github.com/accord-net/framework/tree/master/Samples/Audio/Recorder">source code</a>
</li>
</ul>
<p>The Wave Recorder sample application demonstrates how to use the <a href="http://accord-framework.net/docs/html/T_Accord_Audio_IAudioOutput.htm">IAudioOutput</a> and <a href="http://accord-framework.net/docs/html/T_Accord_Audio_IAudioSource.htm">IAudioSource</a> 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.</p>
<p><a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLYNxBPIMWUIoi2NoA"><img src="https://camo.githubusercontent.com/c622efbb2ad73bba7084209fda162e6e89c032a2/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d617564696f2d7265636f7264696e672e706e67" alt="Wave recorder sample application" data-canonical-src="http://accord-framework.net/images/samples/accord-audio-recording.png"></a><br><sub>Wave recorder sample application.</sub></p>
</div>
            <div id="text-3" class="hidden">
<ul>
<li>Download the <a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLYIjvUqTs-vGVjyRw">application</a>
</li>
<li>Browse the <a href="https://github.com/accord-net/framework/tree/development/Samples/Fuzzy/Robot%20(Inference)">source code</a>
</li>
</ul>
<p><a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLYIjvUqTs-vGVjyRw"><img src="https://camo.githubusercontent.com/f9beedc49a7a4bd086e1977f99894d407bb459cd/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d66757a7a792d696e666572656e63652e706e67" width="400" alt="Robot navigation - inference" data-canonical-src="http://accord-framework.net/images/samples/accord-fuzzy-inference.png"></a>
<br><sub>Robot navigation using Fuzzy logic sample application.</sub></p>
</div>
            <div id="text-4" class="hidden">
<ul>
<li>Download the <a href="https://1drv.ms/u/s!AoiTwBxoR4OAoIptAs5i23ASkuNhdA">application</a>
</li>
<li>Browse the <a href="https://github.com/accord-net/framework/tree/development/Samples/Fuzzy/Sets%20(Fuzzy%2C%20Linguistic)">source code</a>
</li>
</ul>
<p><a href="https://1drv.ms/u/s!AoiTwBxoR4OAoIptAs5i23ASkuNhdA"><img src="https://camo.githubusercontent.com/dff3c187819b80f2c4a69b01de707dfbdea3e82c/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d66757a7a792d736574732e706e67" width="400" alt="Fuzzy sets sample application" data-canonical-src="http://accord-framework.net/images/samples/accord-fuzzy-sets.png"></a>
<br><sub>Fuzzy logic sample application.</sub></p>
</div>
            <div id="text-5" class="hidden">
<ul>
<li>Download the <a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLYJ2jSXDKvzvxcMgQ">application</a>
</li>
<li>Browse the <a href="https://github.com/accord-net/framework/tree/development/Samples/Genetic/Genetic%20Programming">source code</a>
</li>
</ul>
<p><a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLYJ2jSXDKvzvxcMgQ"><img src="https://camo.githubusercontent.com/468ab2db1ad02d04e6ff541c7f064f0076fc1257/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d67656e657469632d70726f6772616d6d696e672e706e67" width="400" alt="Genetic programming sample application" data-canonical-src="http://accord-framework.net/images/samples/accord-genetic-programming.png"></a>
<br><sub>Genetic programming sample application.</sub></p>
</div>
            <div id="text-6" class="hidden">
<ul>
<li>Download the <a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLYOGK89tt0lBJn55g">application</a>
</li>
<li>Browse the <a href="https://github.com/accord-net/framework/tree/development/Samples/Genetic/Travelling%20Salesman%20(GP)">source code</a>
</li>
</ul>
<p><a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLYOGK89tt0lBJn55g"><img src="https://camo.githubusercontent.com/15a9cf6ef3113847467267cb549dc46626c2ca58/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d67656e657469632d74726176656c696e672d73616c65736d616e2e706e67" width="400" alt="Travelling salesman with Genetic programming" data-canonical-src="http://accord-framework.net/images/samples/accord-genetic-traveling-salesman.png"></a>
<br><sub>Travelling salesman with Genetic programming.</sub></p>
</div>
            <div id="text-7" class="hidden">
<ul>
<li>Download the <a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLZcclFYX60m8DasfQ">application</a>
</li>
<li>Browse the <a href="https://github.com/accord-net/framework/tree/master/Samples/MachineLearning/Clustering%20(K-Means)">source code</a>
</li>
</ul>
<p>Shows how to perform image classification using the <a href="http://accord-framework.net/docs/html/T_Accord_Imaging_BagOfVisualWords.htm">Bag-of-Visual-Words (BoW)</a> model
with <a href="http://accord-framework.net/docs/html/T_Accord_Imaging_SpeededUpRobustFeaturesDetector.htm">SURF features</a> and the <a href="http://accord-framework.net/docs/html/T_Accord_MachineLearning_BinarySplit.htm">Binary Split algorithm</a>.</p>
<p>The BoW model is used to transform the many SURF feature points in a image in a single, fixed-length feature vector. The feature vector is then used to train <a href="http://accord-framework.net/docs/html/T_Accord_MachineLearning_VectorMachines_MulticlassSupportVectorMachine.htm">Support Vector Machines (SVMs)</a> using a <a href="http://accord-framework.net/docs/html/N_Accord_Statistics_Kernels.htm">variety of kernels</a>.</p>
<p><a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLZcclFYX60m8DasfQ"><img src="https://camo.githubusercontent.com/4f6a0ec344b35c69f6f565479cfda3f2acee989d/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d696d6167696e672d636c617373696669636174696f6e2e706e67" alt="Image classification sample application" data-canonical-src="http://accord-framework.net/images/samples/accord-imaging-classification.png"></a><br><sub>Image classification sample application.</sub></p>
<p><a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLZcclFYX60m8DasfQ"><img src="https://camo.githubusercontent.com/f178f117388beaea23fa30a8c497deb0cde6a28b/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d696d6167696e672d636c617373696669636174696f6e2d666c6f7765722e706e67" alt="SURF features extracted from one of the images" data-canonical-src="http://accord-framework.net/images/samples/accord-imaging-classification-flower.png"></a><br><sub>SURF features extracted from one of the images.</sub></p>
</div>
            <div id="text-8" class="hidden">
<ul>
<li>Download the <a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLYPZ_SZwtthvUuWJQ">application</a>
</li>
<li>Browse the <a href="https://github.com/accord-net/framework/tree/master/Samples/Imaging/Corners%20(FAST)">source code</a>
</li>
</ul>
<p>The FAST sample application demonstrates how to perform corners detection using the <a href="http://accord-framework.net/docs/html/T_Accord_Imaging_FastCornersDetector.htm">FAST corners detector</a>. As the name implies, the FAST  detector is one of the fastest corners detectors available.</p>
<p><a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLYPZ_SZwtthvUuWJQ"><img src="https://camo.githubusercontent.com/29f2a61a53089916211f52c70f3d44f82a71c4e1/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d696d6167696e672d666173742e706e67" alt="Corner point detection with the FAST algorithm" data-canonical-src="http://accord-framework.net/images/samples/accord-imaging-fast.png"></a><br><sub>Corner point detection with the FAST algorithm.</sub></p>
<p>The current code is based on the excellent <a href="http://www.edwardrosten.com/work/fast.html">Edward Rosten's implementation</a>, and is dual licensed under the <a href="http://en.wikipedia.org/wiki/BSD_licenses">permissive BSD license</a>.</p>
</div>
            <div id="text-9" class="hidden">
<ul>
<li>Download the <a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLYRL8WyStfgPHKCYA">application</a>
</li>
<li>Browse the <a href="https://github.com/accord-net/framework/tree/master/Samples/Imaging/Corners%20(Harris)">source code</a>
</li>
</ul>
<p>The Harris sample application demonstrates how to perform corners detection using the <a href="http://crsouza.blogspot.com/2010/05/harris-corners-detector-in-c.html">Harris algorithm</a>.</p>
<p><a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLYRL8WyStfgPHKCYA"><img src="https://camo.githubusercontent.com/920975495009fba4f9e18db8b64d012b6aa99844/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d696d6167696e672d6861727269732e706e67" alt="Corner point detection with the Harris algorithm" data-canonical-src="http://accord-framework.net/images/samples/accord-imaging-harris.png"></a><br><sub>Corner point detection with the Harris algorithm.</sub></p>
<p>The current implementation supports both <a href="http://accord-framework.net/docs/html/T_Accord_Imaging_HarrisCornerMeasure.htm">Harris and Nobel corner measures</a>. 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.</p>
<p>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.</p>
</div>
            <div id="text-10" class="hidden">
<ul>
<li>Download the <a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLYS8yRj1iR09cKiMg">application</a>
</li>
<li>Browse the <a href="https://github.com/accord-net/framework/tree/master/Samples/Imaging/Corners%20(SURF)">source code</a>
</li>
</ul>
<p>The SURF sample application shows how to use the <a href="http://accord-framework.net/docs/html/T_Accord_Imaging_SpeededUpRobustFeaturesDetector.htm">Speeded-Up Robust Features (SURF) Detector</a>.</p>
<p><a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLYS8yRj1iR09cKiMg"><img src="https://camo.githubusercontent.com/556d6010123ba7c627328cf537bd213ef95ba0fc/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d696d6167696e672d737572662e706e67" alt="Interest point detection with the SURF algorithm" data-canonical-src="http://accord-framework.net/images/samples/accord-imaging-surf.png"></a><br><sub>Interest point detection and description with the SURF algorithm.</sub></p>
<p>The current implementation is based on the excellent <a href="http://www.chrisevansdev.com/computer-vision-opensurf.html">OpenSURF library</a> by Christopher Evans. The framework version, however, comes with further performance optimizations.</p>
</div>
            <div id="text-11" class="hidden">
<ul>
<li>Download the <a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLYQdS2uoBpxVp-moA">application</a>
</li>
<li>Browse the <a href="https://github.com/accord-net/framework/tree/development/Samples/Imaging/Detection%20(Blobs)">source code</a>
</li>
</ul>
<p>The Detection (blobs) sample application shows how to use the <a href="http://accord-framework.net/docs/html/T_Accord_Imaging_BlobCounter.htm">BlobCounter</a>, <a href="http://accord-framework.net/docs/html/T_Accord_Imaging_Blob.htm">Blob</a> and <a href="http://accord-framework.net/docs/html/T_Accord_Math_Geometry_GrahamConvexHull.htm">GrahamConvexHull classes</a> to obtain the shapes of different objects in an input image.</p>
<p><a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLYQdS2uoBpxVp-moA"><img src="https://camo.githubusercontent.com/33936d6e9d960fc583ea474e98d24a4f25b01dac/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d696d6167696e672d626c6f62732e706e67" width="400" alt="Blobs identification with the BlobCounter class" data-canonical-src="http://accord-framework.net/images/samples/accord-imaging-blobs.png"></a>
<br><sub>Blobs identification with the BlobCounter class.</sub></p>
</div>
            <div id="text-12" class="hidden">
<ul>
<li>Download the <a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLYTkKk4qUNJHM9ZAw">application</a>
</li>
<li>Browse the <a href="https://github.com/accord-net/framework/tree/development/Samples/Imaging/Detection%20(Shape)">source code</a>
</li>
</ul>
<p>The Detection (shapes) sample application shows how to use the <a href="http://accord-framework.net/docs/html/T_Accord_Math_Geometry_SimpleShapeChecker.htm">SimpleShapeChecker</a> to obtain the shapes of different objects in an input image.</p>
<p><a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLYTkKk4qUNJHM9ZAw"><img src="https://camo.githubusercontent.com/31061091707952d6271e2dd56a0686f69f70e90c/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d696d6167696e672d7368617065732e706e67" width="400" alt="Shape identification with the SimpleShapeChecker class" data-canonical-src="http://accord-framework.net/images/samples/accord-imaging-shapes.png"></a>
<br><sub>Object shape detection through blob identification.</sub></p>
</div>
            <div id="text-13" class="hidden">
<ul>
<li>Download the <a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLYWzQs0dNEq1OOgiA">application</a>
</li>
<li>Browse the <a href="https://github.com/accord-net/framework/tree/development/Samples/Imaging/Filters%20(Images)">source code</a>
</li>
</ul>
<p>The image filters sample application shows how to use the <a href="http://accord-framework.net/docs/html/N_Accord_Imaging_Filters.htm">more than 80 image filters currently available at Accord.Imaging.Fitlers</a>.</p>
<p><a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLYTkKk4qUNJHM9ZAw"><img src="https://camo.githubusercontent.com/e3dbc784a9e23d9c0aa01feb97eff6464b611dcb/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d696d6167696e672d66696c746572732e706e67" width="400" alt="Image filtering with the Accord.Imaging.Filters namespace" data-canonical-src="http://accord-framework.net/images/samples/accord-imaging-filters.png"></a>
<br><sub>Image filter applications.</sub></p>
</div>
            <div id="text-14" class="hidden">
<ul>
<li>Download the <a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLYUZRXpxMoLzoYaDQ">application</a>
</li>
<li>Browse the <a href="https://github.com/accord-net/framework/tree/development/Samples/Imaging/Hough%20Transform">source code</a>
</li>
</ul>
<p><a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLYYnHYopCgI3uTDbQ"><img src="https://camo.githubusercontent.com/023ea4e4029950f03287eff34d45893fd7d68ecf/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d696d6167696e672d686f7567682e706e67" width="400" alt="Hough transform for straight line and/or circle detection" data-canonical-src="http://accord-framework.net/images/samples/accord-imaging-hough.png"></a>
<br><sub>Hough transform for straight line and/or circle detection.</sub></p>
</div>
            <div id="text-15" class="hidden">
<ul>
<li>Download the <a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLYV-_drfQWUgjJjcA">application</a>
</li>
<li>Browse the <a href="https://github.com/accord-net/framework/tree/development/Samples/Imaging/Image%20Viewer">source code</a>
</li>
</ul>
<p><a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLYYnHYopCgI3uTDbQ"><img src="https://camo.githubusercontent.com/145e8faa629c4065c0de62b7ddf80b206d3621ec/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d696d6167696e672d696d6167652d7669657765722e706e67" width="400" alt="Image viewer sample application" data-canonical-src="http://accord-framework.net/images/samples/accord-imaging-image-viewer.png"></a>
<br><sub>Image viewer sample application.</sub></p>
</div>
            <div id="text-16" class="hidden">
<ul>
<li>Download the <a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLYYnHYopCgI3uTDbQ">application</a>
</li>
<li>Browse the <a href="https://github.com/accord-net/framework/tree/master/Samples/Imaging/Panorama%20(FREAK)">source code</a>
</li>
</ul>
<p>Demonstrates how to perform automatic image stitching by interest point matching. The actual stitching uses many parts of the framework, such as the <a href="http://accord-framework.net/docs/html/T_Accord_Imaging_FastRetinaKeypointDetector.htm">FREAK feature detector</a>, <a href="http://accord-framework.net/docs/html/T_Accord_Imaging_RansacHomographyEstimator.htm">RANSAC</a>, <a href="http://accord-framework.net/docs/html/T_Accord_Imaging_KNearestNeighborMatching.htm">k-nearest neighbor matching</a>, homography estimation and <a href="http://accord-framework.net/docs/html/T_Accord_Imaging_Filters_Blend.htm">linear gradient image blending</a>.</p>
<p><a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLYYnHYopCgI3uTDbQ"><img src="https://camo.githubusercontent.com/e7dc85e4865af86ccf0463305eee0bb10022acc7/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d696d6167696e672d70616e6f72616d612d667265616b2e706e67" alt="Image stitching using the FREAK feature detection and extraction algorithm." data-canonical-src="http://accord-framework.net/images/samples/accord-imaging-panorama-freak.png"></a><br><sub>Image stitching using the FREAK feature detection and extraction algorithm.</sub></p>
</div>
            <div id="text-17" class="hidden">
<ul>
<li>Download the <a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLYaZiAQJhWuOWPDqw">application</a>
</li>
<li>Browse the <a href="https://github.com/accord-net/framework/tree/master/Samples/Imaging/Panorama%20(Harris)">source code</a>
</li>
</ul>
<p>Demonstrates how to perform automatic image stitching by interest point matching. The actual stitching uses many parts of the framework, such as the <a href="http://accord-framework.net/docs/html/T_Accord_Imaging_HarrisCornersDetector.htm">Harris corners detector</a>, <a href="http://accord-framework.net/docs/html/T_Accord_Imaging_RansacHomographyEstimator.htm">RANSAC</a>, <a href="http://accord-framework.net/docs/html/T_Accord_Imaging_CorrelationMatching.htm">correlation window matching</a>, homography estimation and <a href="http://accord-framework.net/docs/html/T_Accord_Imaging_Filters_Blend.htm">linear gradient image blending</a>.</p>
<p><a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLYaZiAQJhWuOWPDqw"><img src="https://camo.githubusercontent.com/7dbd9533ca0eb95bba248cdfd2584fe9210ce406/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d696d6167696e672d70616e6f72616d612d6861727269732e706e67" alt="Image stitching using Harris corners detection and correlation matching." data-canonical-src="http://accord-framework.net/images/samples/accord-imaging-panorama-harris.png"></a><br><sub>Image stitching using Harris corners detection and correlation matching.</sub></p>
<p>For more details about the method, please be sure to read the accompanying article on how to perform <a href="http://www.codeproject.com/Articles/95453/Automatic-Image-Stitching-with-Accord-NET">automatic image stitching by interest point matching in C#</a>.</p>
</div>
            <div id="text-18" class="hidden">
<ul>
<li>Download the <a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLYeOA4wERsoD_TJDA">application</a>
</li>
<li>Browse the <a href="https://github.com/accord-net/framework/tree/master/Samples/Imaging/Panorama%20(SURF)">source code</a>
</li>
</ul>
<p>Demonstrates how to perform automatic image stitching by interest point matching. The actual stitching uses many parts of the framework, such as the <a href="http://accord-framework.net/docs/html/T_Accord_Imaging_SpeededUpRobustFeaturesDetector.htm">SURF feature detector</a>, <a href="http://accord-framework.net/docs/html/T_Accord_Imaging_RansacHomographyEstimator.htm">RANSAC</a>, <a href="http://accord-framework.net/docs/html/T_Accord_Imaging_KNearestNeighborMatching.htm">k-nearest neighbor matching</a>, homography estimation and <a href="http://accord-framework.net/docs/html/T_Accord_Imaging_Filters_Blend.htm">linear gradient image blending</a>.</p>
<p><a href="https://1drv.ms/u/s!AoiTwBxoR4OAoIpozWUHmUDHoYCnAg"><img src="https://camo.githubusercontent.com/48d7480845d3e64641e858a0bc2d81b0b63e9bd8/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d696d6167696e672d70616e6f72616d612d737572662e706e67" alt="Image stitching using SURF features and k-nearest neighbor matching." data-canonical-src="http://accord-framework.net/images/samples/accord-imaging-panorama-surf.png"></a><br><sub>Image stitching using SURF features and k-nearest neighbor matching.</sub></p>
</div>
            <div id="text-19" class="hidden">
<ul>
<li>Download the <a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLYZrgyLbdkUICMCiA">application</a>
</li>
<li>Browse the <a href="https://github.com/accord-net/framework/tree/development/Samples/Imaging/Pose%20Estimation%20(POSIT)">source code</a>
</li>
</ul>
<p><a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLYZrgyLbdkUICMCiA"><img src="https://camo.githubusercontent.com/fdc438b46a3ba1705c09f19c18d0d5c4a379a348/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d696d6167696e672d706f73652d657374696d6174696f6e2d706f7369742e706e67" width="400" alt="Pose estimation with POSIT and coplanar POSIT algorithms" data-canonical-src="http://accord-framework.net/images/samples/accord-imaging-pose-estimation-posit.png"></a>
<br><sub>Pose estimation with POSIT and coplanar POSIT algorithms.</sub></p>
</div>
            <div id="text-20" class="hidden">
<ul>
<li>Download the <a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLYXyZHJgICUyXtxxA">application</a>
</li>
<li>Browse the <a href="https://github.com/accord-net/framework/tree/development/Samples/Imaging/Texture">source code</a>
</li>
</ul>
<p><a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLYXyZHJgICUyXtxxA"><img src="https://camo.githubusercontent.com/3fd943ec49287f699bf652398699c552b297e3b3/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d696d6167696e672d74657874757265732e706e67" width="400" alt="Texture generation" data-canonical-src="http://accord-framework.net/images/samples/accord-imaging-textures.png"></a>
<br><sub>Texture generation.</sub></p>
</div>
            <div id="text-21" class="hidden">
<ul>
<li>Download the <a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLYcGucTvgABcA_tOQ">application</a>
</li>
<li>Browse the <a href="https://github.com/accord-net/framework/tree/master/Samples/Imaging/Wavelets">source code</a>
</li>
</ul>
<p>The Wavelet sample application shows how to use the <a href="http://accord-framework.net/docs/html/T_Accord_Imaging_Filters_WaveletTransform.htm">Wavelet transform filter</a> to process images using <a href="http://accord-framework.net/docs/html/N_Accord_Math_Wavelets.htm">wavelet transforms</a> such as the <a href="http://accord-framework.net/docs/html/T_Accord_Math_Wavelets_Haar.htm">Haar</a> and <a href="http://accord-framework.net/docs/html/T_Accord_Math_Wavelets_CDF97.htm">CDF9/7</a>.</p>
<p><a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLYcGucTvgABcA_tOQ"><img src="https://camo.githubusercontent.com/6437a6e36f4db44276e3ba5503bb684263deaa35/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d696d6167696e672d776176656c6574732e706e67" alt="Wavelets sample application." data-canonical-src="http://accord-framework.net/images/samples/accord-imaging-wavelets.png"></a><br><sub>Wavelets sample application.</sub></p>
</div>
            <div id="text-22" class="hidden">
<ul>
<li>Download the <a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLYbSpSs3w0i__dgIA">application</a>
</li>
<li>Browse the <a href="https://github.com/accord-net/framework/tree/development/Samples/MachineLearning/Animat">source code</a>
</li>
</ul>
<p><a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLYdur5_4-s-TyjzIw"><img src="https://camo.githubusercontent.com/823e3e55ce6f3f52887e0bbfcc607b20759781f8/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d6d616368696e656c6561726e696e672d616e696d61742e706e67" width="400" alt="Animat example for QLearning and Sarsa" data-canonical-src="http://accord-framework.net/images/samples/accord-machinelearning-animat.png"></a>
<br><sub>Animat example for QLearning and Sarsa.</sub></p>
</div>
            <div id="text-23" class="hidden">
<ul>
<li>Download the <a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLYdur5_4-s-TyjzIw">application</a>
</li>
<li>Browse the <a href="https://github.com/accord-net/framework/tree/master/Samples/MachineLearning/Classification%20(SVMs)">source code</a>
</li>
</ul>
<p>This sample application shows how to use <a href="http://accord-framework.net/docs/html/T_Accord_MachineLearning_VectorMachines_KernelSupportVectorMachine.htm">Kernel Support Vector Machines (kSVMs)</a> to solve a classification problem. The sample application comes with default sample data with can be loaded in the File -&gt; Open menu.</p>
<p><a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLYdur5_4-s-TyjzIw"><img src="https://camo.githubusercontent.com/d673e59f5a94860ff8b7dbbad6440b6be32d0f14/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d6d616368696e656c6561726e696e672d636c617373696669636174696f6e2d73766d2d312e706e67" alt="Data classification with SVMs" data-canonical-src="http://accord-framework.net/images/samples/accord-machinelearning-classification-svm-1.png"></a><br><sub>Sample nonlinear problem.</sub></p>
<p>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 Ying-Yang classification data generated by a <a href="http://accord-framework.net/docs/html/M_Accord_Statistics_Kernels_Gaussian_Estimate.htm">heuristically</a> initialized <a href="http://accord-framework.net/docs/html/T_Accord_Statistics_Kernels_Gaussian.htm">Gaussian-kernel</a> SVM after it has been trained using <a href="http://accord-framework.net/docs/html/T_Accord_MachineLearning_VectorMachines_Learning_SequentialMinimalOptimization.htm">Sequential Minimal Optimization (SMO)</a>. The framework offers an <a href="http://accord-framework.net/docs/html/N_Accord_Statistics_Kernels.htm">extensive list of kernel functions to choose from</a>.</p>
<p><a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLYdur5_4-s-TyjzIw"><img src="https://camo.githubusercontent.com/1d4ab08d3cc232cd0c4c90de0b67832c124b2af0/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d6d616368696e656c6561726e696e672d636c617373696669636174696f6e2d73766d2e706e67" alt="Data classification with SVMs" data-canonical-src="http://accord-framework.net/images/samples/accord-machinelearning-classification-svm.png"></a><br><sub>Decision surface generated by a Gaussian SVM.</sub></p>
<p><a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLYdur5_4-s-TyjzIw"><img src="https://camo.githubusercontent.com/52f4641a810f9d7638697961602ff826c51cf37f/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d6d616368696e656c6561726e696e672d636c617373696669636174696f6e2d73766d2d332e706e67" alt="Sparse support vectors." data-canonical-src="http://accord-framework.net/images/samples/accord-machinelearning-classification-svm-3.png"></a><br><sub>Sparse support vectors.</sub></p>
<p><a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLYdur5_4-s-TyjzIw"><img src="https://camo.githubusercontent.com/a009aa685bf801fe53286f420d67d56df2c2934c/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d6d616368696e656c6561726e696e672d636c617373696669636174696f6e2d73766d2d342e706e67" alt="SVM Classification results" data-canonical-src="http://accord-framework.net/images/samples/accord-machinelearning-classification-svm-4.png"></a><br><sub>SVM Classification results.</sub></p>
</div>
            <div id="text-24" class="hidden">
<ul>
<li>Download the <a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLYfhluV6m5Xj3jw3w">application</a>
</li>
<li>Browse the <a href="https://github.com/accord-net/framework/tree/master/Samples/MachineLearning/Clustering%20(GMM)">source code</a>
</li>
</ul>
<p>This sample application shows how to use <a href="http://accord-framework.net/docs/html/T_Accord_MachineLearning_GaussianMixtureModel.htm">Gaussian Mixture Models</a> to perform clustering and classification using soft-decision margins.</p>
<p><a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLYfhluV6m5Xj3jw3w"><img src="https://camo.githubusercontent.com/2bd6339dfbe653f320ce9ea8c4ec44e673a38274/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d6d616368696e656c6561726e696e672d636c7573746572696e672d676d6d2e706e67" alt="Clustering data with GMMs" data-canonical-src="http://accord-framework.net/images/samples/accord-machinelearning-clustering-gmm.png"></a><br><sub>Clustering data with GMMs.</sub></p>
</div>
            <div id="text-25" class="hidden">
<ul>
<li>Download the <a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLYgsNszvSjWpHAuEA">application</a>
</li>
<li>Browse the <a href="https://github.com/accord-net/framework/tree/development/Samples/MachineLearning/Clustering%20(K-Means)">source code</a>
</li>
</ul>
<p>This sample application shows how to use the <a href="http://accord-framework.net/docs/html/T_Accord_MachineLearning_KMeans.htm">K-Means clustering algorithm</a> and the <a href="http://accord-framework.net/docs/html/T_Accord_MachineLearning_MeanShift.htm">mean shift clustering algorithm</a> to perform <a href="http://crsouza.blogspot.com.br/2010/10/k-means-clustering.html">color clustering</a>, reducing the number of distinct colors in a given image.</p>
<p><a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLYgsNszvSjWpHAuEA"><img src="https://camo.githubusercontent.com/866948db5c92ec4b19da82b83207cf7e154085a7/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d6d616368696e656c6561726e696e672d636c7573746572696e672d6d65616e73686966742d6b6d65616e732e706e67" alt="Color clustering with K-Means and MeanShift" data-canonical-src="http://accord-framework.net/images/samples/accord-machinelearning-clustering-meanshift-kmeans.png"></a><br><sub>Color clustering with K-Means and MeanShift.</sub></p>
</div>
            <div id="text-26" class="hidden">
<ul>
<li>Download the <a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLYhQI9vtCbvT4G7Gg">application</a>
</li>
<li>Browse the <a href="https://github.com/accord-net/framework/tree/development/Samples/MachineLearning/Clustering%202D%20(K-Means%2C%20MeanShift%2C%20GMM)">source code</a>
</li>
</ul>
<p>This sample application shows how to use the <a href="http://accord-framework.net/docs/html/T_Accord_MachineLearning_KMeans.htm">K-Means</a>, <a href="http://accord-framework.net/docs/html/T_Accord_MachineLearning_MeanShift.htm">MeanShift</a>, or <a href="http://accord-framework.net/docs/html/T_Accord_MachineLearning_GaussianMixtureModel.htm">Gaussian Mixture Models</a> to perform clustering of data points in a 2D space.</p>
<p><a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLYgsNszvSjWpHAuEA"><img src="https://camo.githubusercontent.com/8000632da1b1e6a0e39445cc6eaba8dc84f1e917/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d6d616368696e656c6561726e696e672d636c7573746572696e672d32642e706e67" width="400" alt="Data clustering with K-Means, MeanShift and GMMs" data-canonical-src="http://accord-framework.net/images/samples/accord-machinelearning-clustering-2d.png"></a>
<br><sub>Data clustering with K-Means, MeanShift and GMMs.</sub></p>
</div>
            <div id="text-27" class="hidden">
<ul>
<li>Download the <a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLYisQ0k_oH3aLHGzg">application</a>
</li>
<li>Browse the <a href="https://github.com/accord-net/framework/tree/master/Samples/MachineLearning/Decision%20Trees">source code</a>
</li>
</ul>
<p>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 computer-aided diagnostics and credit analysis.</p>
<p>Here is a <a href="http://crsouza.com/2012/01/decision-trees-in-c.html">detailed explanation on Decision Trees</a>.</p>
<p><a href="https://1drv.ms/u/s!AoiTwBxoR4OAoIpVAKyIrSl89VE5Yw"><img src="https://camo.githubusercontent.com/808a3bb92d2c93e846d187a0a67a6be3a3f1884c/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d6d616368696e656c6561726e696e672d636c617373696669636174696f6e2d74726565732e706e67" alt="Data classification with decision trees" data-canonical-src="http://accord-framework.net/images/samples/accord-machinelearning-classification-trees.png"></a><br><sub>Data classification with Decision Trees.</sub></p>
</div>
            <div id="text-28" class="hidden">
<ul>
<li>Download the <a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLYjyngVTaPNFcFfmA">application</a>
</li>
<li>Browse the <a href="https://github.com/accord-net/framework/tree/master/Samples/MachineLearning/Feature%20Selection%20(SVMs)">source code</a>
</li>
</ul>
<p>Feature selection using sparse L1-regularized logistic support vector machines. This sample application shows how to create special linear SVMs with logistic functions to perform feature selection.</p>
<p><a href="accord-machinelearning-feature-selection-(svms)"><img src="https://camo.githubusercontent.com/c22fe34eb7e9347857e6632e54e9eb8177ee40c7/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d6d616368696e656c6561726e696e672d666561747572652d73656c656374696f6e2e706e67" alt="A problem that can be perfectly separated using only X." data-canonical-src="http://accord-framework.net/images/samples/accord-machinelearning-feature-selection.png"></a><br><sub>A problem that can be perfectly separated using only X.</sub></p>
<p><a href="accord-machinelearning-feature-selection-(svms)"><img src="https://camo.githubusercontent.com/1f0f9a4dbe9c2a341f823f0c570764625150ed7d/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d6d616368696e656c6561726e696e672d666561747572652d73656c656374696f6e2d322e706e67" alt="The machine accurately says that X is the most important feature." data-canonical-src="http://accord-framework.net/images/samples/accord-machinelearning-feature-selection-2.png"></a><br><sub>The machine accurately says that X is the most important feature.</sub></p>
</div>
            <div id="text-29" class="hidden">
<ul>
<li>Download the <a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLYk6WH7bZ_Fnx3tpg">application</a>
</li>
<li>Browse the <a href="https://github.com/accord-net/framework/tree/master/Samples/MachineLearning/Fitting%20(RANSAC)">source code</a>
</li>
</ul>
<p>This sample application shows how to use the <a href="http://accord-framework.net/docs/html/T_Accord_MachineLearning_RANSAC_1.htm">Random Sample Consensus (RANSAC)</a> algorithm to fit <a href="http://accord-framework.net/docs/html/N_Accord_Statistics_Models_Regression_Linear.htm">linear regression models</a>. The algorithm works with any model or function, producing a robust version of the model which is less sensitive to outliers. Here is a <a href="http://crsouza.com/2010/06/random-sample-consensus-ransac-in-c.html">detailed explanation on how RANSAC works</a>.</p>
<p><a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLYk6WH7bZ_Fnx3tpg"><img src="https://camo.githubusercontent.com/00795c3ae7001c3829cab7b4ff642408024405c8/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d6d616368696e656c6561726e696e672d72616e7361632e706e67" alt="Robust linear estimation with RANSAC" data-canonical-src="http://accord-framework.net/images/samples/accord-machinelearning-ransac.png"></a><br><sub>Robust linear estimation with RANSAC.</sub></p>
</div>
            <div id="text-30" class="hidden">
<ul>
<li>Download the <a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLYlmCe5rdKfhxT5MQ">application</a>
</li>
<li>Browse the <a href="https://github.com/accord-net/framework/tree/development/Samples/MachineLearning/Gestures%20(SVMs)">source code</a>
</li>
</ul>
<p>Demonstrates how to use Support Vector Machines (SVMs) with the Dynamic Time Warping (DTW) kernel to recognize sequences of discrete observations.</p>
<p><a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLYlmCe5rdKfhxT5MQ"><img src="https://camo.githubusercontent.com/83a326b3cc6595758e85a5e561448f3610d0dfe4/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d6d616368696e656c6561726e696e672d67657374757265732d73766d2e706e67" alt="Dynamic Time Warping sample application" data-canonical-src="http://accord-framework.net/images/samples/accord-machinelearning-gestures-svm.png"></a></p>
</div>
            <div id="text-31" class="hidden">
<ul>
<li>Download the <a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLYmDj1xkoRRAYKEig">application</a>
</li>
<li>Browse the <a href="https://github.com/accord-net/framework/tree/master/Samples/MachineLearning/Handwriting%20(K-Means)">source code</a>
</li>
</ul>
<p>This sample application shows how to teach <a href="http://accord-framework.net/docs/html/T_Accord_MachineLearning_VectorMachines_MulticlassSupportVectorMachine.htm">Multi-class Support Vector Machines</a> using <a href="http://accord-framework.net/docs/html/T_Accord_MachineLearning_VectorMachines_Learning_SequentialMinimalOptimization.htm">Sequential Minimal Optimization</a> to recognize handwritten digits from the UCI's Optdigits dataset.</p>
<p><a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLYmDj1xkoRRAYKEig"><img src="https://camo.githubusercontent.com/3162ebcc0cbb19db226122fe2541353f41c27fe1/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d6d616368696e656c6561726e696e672d68616e6477726974696e672d73766d2e706e67" alt="Handwritten digits recognition with SVMs" data-canonical-src="http://accord-framework.net/images/samples/accord-machinelearning-handwriting-svm.png"></a><br><sub>Handwritten digits recognition with SVMs.</sub></p>
</div>
            <div id="text-32" class="hidden">
<ul>
<li>Download the <a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLYptO2tXjOtFPEfkw">application</a>
</li>
<li>Browse the <a href="https://github.com/accord-net/framework/tree/master/Samples/MachineLearning/Liblinear%20(SVMs)">source code</a>
</li>
</ul>
<p>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 <em>all liblinear algorithms in C#</em>, except for one. Those include:</p>
<pre><code>  0 — L2-regularized logistic regression (primal)
  1 – L2-regularized L2-loss support vector classification (dual) 
  2 — L2-regularized L2-loss support vector classification (primal) 
  3 — L2-regularized L1-loss support vector classification (dual) 
  4 –
  5 — L1-regularized L2-loss support vector classification
  6 — L1-regularized logistic regression 
  7 — L2-regularized logistic regression (dual) for regression 
  11 — L2-regularized L2-loss support vector regression (primal)
  12 — L2-regularized L2-loss support vector regression (dual)
  13 — L2-regularized L1-loss support vector regression (dual)
</code></pre>
<p>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.</p>
<p><a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLYptO2tXjOtFPEfkw"><img src="https://camo.githubusercontent.com/d59a564dffe8c2b64468b08fdc78cf4595f5c3f6/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d6d616368696e656c6561726e696e672d6c69626c696e6561722e706e67" alt="Liblinear in C#" data-canonical-src="http://accord-framework.net/images/samples/accord-machinelearning-liblinear.png"></a><br><sub>Pure C# liblinear application.</sub></p>
</div>
            <div id="text-33" class="hidden">
<ul>
<li>Download the <a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLYokv3rDw-BOt2kBw">application</a>
</li>
<li>Browse the <a href="https://github.com/accord-net/framework/tree/master/Samples/MachineLearning/Naive%20Bayes">source code</a>
</li>
</ul>
<p>This application shows how to use <a href="http://accord-framework.net/docs/html/T_Accord_MachineLearning_Bayes_NaiveBayes.htm">Naive Bayes</a> for binary classification tasks.</p>
<p><a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLYokv3rDw-BOt2kBw"><img src="https://camo.githubusercontent.com/fff00e1c8c45b924f24e5598aadf7f6e0e21af9f/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d6d616368696e656c6561726e696e672d636c617373696669636174696f6e2d6e62617965732e706e67" alt="Data classification with Naive Bayes" data-canonical-src="http://accord-framework.net/images/samples/accord-machinelearning-classification-nbayes.png"></a><br><sub>Data classification with Naive Bayes.</sub></p>
</div>
            <div id="text-34" class="hidden">
<ul>
<li>Download the <a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLYqcLZd236Z_BlIXg">application</a>
</li>
<li>Browse the <a href="https://github.com/accord-net/framework/tree/master/Samples/MachineLearning/Regression%20(SVMs)">source code</a>
</li>
</ul>
<p>Function regression using (Kernel) Support Vector Machines.</p>
<p><a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLYqcLZd236Z_BlIXg"><img src="https://camo.githubusercontent.com/1dada516366e675364e79b286e47a2474728a7d4/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d6d616368696e656c6561726e696e672d72656772657373696f6e2d73766d2e706e67" alt="Function regression with SVMs" data-canonical-src="http://accord-framework.net/images/samples/accord-machinelearning-regression-svm.png"></a><br><sub>Function regression with SVMs.</sub></p>
</div>
            <div id="text-35" class="hidden">
<ul>
<li>Download the <a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLYn50q8w_nZ9Vzn3Q">application</a>
</li>
<li>Browse the <a href="https://github.com/accord-net/framework/tree/development/Samples/Math/Matrices">source code</a>
</li>
</ul>
<p><a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLYtjm_7EGcd1ic1HA"><img src="https://camo.githubusercontent.com/2ab2d9a35f7a1fed9f525767ad04b97dac28061a/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d6d6174682d6d61747269786f7065726174696f6e732d696d672e706e67" alt="Matrix usage examples" data-canonical-src="http://accord-framework.net/images/samples/accord-math-matrixoperations-img.png"></a><br><sub>Matrix usage examples.</sub></p>
</div>
            <div id="text-36" class="hidden">
<ul>
<li>Download the <a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLYsF75VT6FX_AOvkg">application</a>
</li>
<li>Browse the <a href="https://github.com/accord-net/framework/tree/development/Samples/Math/Pose%20Coordinates%20(POSIT)">source code</a>
</li>
</ul>
<p><a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLYtjm_7EGcd1ic1HA"><img src="https://camo.githubusercontent.com/97defd469ef0d2a61d07fc3ff28d2e26f74b5adc/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d6d6174682d706f73652d657374696d6174696f6e2d706f7369742e706e67" alt="Pose coordinates estimation using POSIT" data-canonical-src="http://accord-framework.net/images/samples/accord-math-pose-estimation-posit.png"></a><br><sub>Pose coordinates estimation using POSIT.</sub></p>
</div>
            <div id="text-37" class="hidden">
<ul>
<li>Download the <a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLYtjm_7EGcd1ic1HA">application</a>
</li>
<li>Browse the <a href="https://github.com/accord-net/framework/tree/master/Samples/Math/Solver%20(Goldfarb-Idnani)">source code</a>
</li>
</ul>
<p>Quadratic Programming (QP) problem solving using the dual method of Goldfarb and
Idnani. Translated from the original Fortran code by Berwin A. Turlach.</p>
<p><a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLYtjm_7EGcd1ic1HA"><img src="https://camo.githubusercontent.com/9d68882bb620991667f6b93a0dfae9a4ba0ebb85/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d6d6174682d736f6c7665722d28676f6c64666172622d69646e616e69292e706e67" alt="Quadratic Programming Solver" data-canonical-src="http://accord-framework.net/images/samples/accord-math-solver-(goldfarb-idnani).png"></a><br><sub>Quadratic Programming Solver.</sub></p>
</div>
            <div id="text-38" class="hidden">
<ul>
<li>Download the <a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLYrl22RsB-M_GZD8w">application</a>
</li>
<li>Browse the <a href="https://github.com/accord-net/framework/tree/development/Samples/Math/Kinematics">source code</a>
</li>
</ul>
<p>Demonstrates how to use Denavit-Hartenberg parameters and equations to model kinematic chains.</p>
<p><a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLYrl22RsB-M_GZD8w"><img src="https://camo.githubusercontent.com/608e3f860ff60ee4fa340b8e01fd57922a0a882a/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d6d6174682d64656e617669742e706e67" alt="Denavit-Hartenberg forward kinematics sample application." data-canonical-src="http://accord-framework.net/images/samples/accord-math-denavit.png"></a><br><sub>Denavit-Hartenberg forward kinematics sample application.</sub></p>
</div>
            <div id="text-39" class="hidden">
<ul>
<li>Download the <a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLYw_iPV36d-k-XRDA">application</a>
</li>
<li>Browse the <a href="https://github.com/accord-net/framework/tree/development/Samples/Neuro/Levenberg-Marquardt">source code</a>
</li>
</ul>
<p>This sample application shows how to use the Levenberg-Marquardt learning algorithm together with Bayesian regularization to teach a feed-forward neural network.</p>
<p><a href="http://www.youtube.com/watch?v=wHlhmP7nN9o"><img src="https://camo.githubusercontent.com/db0a7de503753de02185d274cb0a31c3338d3336/687474703a2f2f696d672e796f75747562652e636f6d2f76692f77486c686d50376e4e396f2f302e6a7067" alt="Levenberg-Marquardt neural network learning" data-canonical-src="http://img.youtube.com/vi/wHlhmP7nN9o/0.jpg"></a><br><sub>Data classification with Neural Networks using the Levenberg-Marquardt algorithm with and without Bayesian regularization.</sub></p>
</div>
            <div id="text-40" class="hidden">
<ul>
<li>Download the <a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLYuaRhhbISY31Z6Og">application</a>
</li>
<li>Browse the <a href="https://github.com/accord-net/framework/tree/master/Samples/Neuro/Deep%20Learning">source code</a>
</li>
</ul>
<p>This sample application shows how to learn Deep Belief Networks using Restricted Boltzmann Machines and the Contrastive-Divergence algorithm. However, please note that this approach has been deprecated in favor of learning Deep Neural Networks with ReLU and BatchNorm directly using SGD.</p>
<p><a href="http://www.youtube.com/watch?v=m7dA86oyJvU"><img src="https://camo.githubusercontent.com/743ac11379c9d71336f7c723e76eab825c11c0e5/687474703a2f2f696d672e796f75747562652e636f6d2f76692f6d37644138366f794a76552f302e6a7067" alt="Deep neural network learning" data-canonical-src="http://img.youtube.com/vi/m7dA86oyJvU/0.jpg"></a><br><sub>Deep neural network learning.</sub></p>
</div>
            <div id="text-41" class="hidden">
<ul>
<li>Download the <a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLYyL_C8AtpjwMhbXQ">application</a>
</li>
<li>Browse the <a href="https://github.com/accord-net/framework/tree/master/Samples/Neuro/Levenberg-Marquardt">source code</a>
</li>
</ul>
<p>An adaptation of the original AForge.NET Neuro sample applications to work with
Levenberg-Marquardt instead of Backpropagation. Includes solutions for approximation,
time-series prediction and the exclusive-or (XOR) problem using neural networks
trained by Levenberg-Marquardt. For more information regarding the method of Levenberg-Marquardt, please take a look on <a href="http://crsouza.blogspot.com.br/2009/11/neural-network-learning-by-levenberg_18.html">Neural Network Learning by the Leveberg-Marquardt Algorithm with Bayesian Regularization</a>.</p>
<p><a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLYyL_C8AtpjwMhbXQ"><img src="https://camo.githubusercontent.com/7ae12aa7323a1660eaffa9d4821beaa7cc14d761/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d6e6575726f2d6c6576656e626572672d6d61727175617264742e706e67" alt="Levenberg-Marquardt algorithm for Approximation, Time Series, and the XOR problems" data-canonical-src="http://accord-framework.net/images/samples/accord-neuro-levenberg-marquardt.png"></a><br><sub>Levenberg-Marquardt algorithm for Approximation, Time Series, and the XOR problems.</sub></p>
</div>
            <div id="text-42" class="hidden">
<ul>
<li>Download the <a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLYxh47LtrIpUkbLig">application</a>
</li>
<li>Browse the <a href="https://github.com/accord-net/framework/tree/development/Samples/Neuro/Perceptron">source code</a>
</li>
</ul>
<p><a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLYxh47LtrIpUkbLig"><img src="https://camo.githubusercontent.com/4326a50854c31c46b848df17bdc4393412894a27/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d6e6575726f2d70657263657074726f6e2e706e67" width="400" alt="Delta rule learning, perceptron learning and multilayer perceptron learning" data-canonical-src="http://accord-framework.net/images/samples/accord-neuro-perceptron.png"></a>
<br><sub>Delta rule learning, perceptron learning and multilayer perceptron learning.</sub></p>
</div>
            <div id="text-43" class="hidden">
<ul>
<li>Download the <a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLYzKcsLD6o-V5qqwg">application</a>
</li>
<li>Browse the <a href="https://github.com/accord-net/framework/tree/master/Samples/Neuro/Resilient-Backpropagation">source code</a>
</li>
</ul>
<p>An adaptation of the original AForge.NET Neuro sample applications to work with
Resilient Backpropagation instead of Backpropagation. Includes solutions for approximation, time-series prediction and the exclusive-or (XOR) problem using neural networks trained by Resilient Backpropagation.</p>
<p><a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLYzKcsLD6o-V5qqwg"><img src="https://camo.githubusercontent.com/e2f22fc845402b9c6bb1f5f476890ecd2bbacf93/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d6e6575726f2d726573696c69656e742d6261636b70726f7061676174696f6e2e706e67" alt="RProp algorithm for Approximation, Time Series, and the XOR problems" data-canonical-src="http://accord-framework.net/images/samples/accord-neuro-resilient-backpropagation.png"></a><br><sub>RProp algorithm for Approximation, Time Series, and the XOR problems.</sub></p>
</div>
            <div id="text-44" class="hidden">
<ul>
<li>Download the <a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLY0mgCZXRaHUPY59Q">application</a>
</li>
<li>Browse the <a href="https://github.com/accord-net/framework/tree/development/Samples/Neuro/Self-Organizing%20Maps%20(SOM)">source code</a>
</li>
</ul>
<p><a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLY0mgCZXRaHUPY59Q"><img src="https://camo.githubusercontent.com/6a5ea96eefcdd575c0f2460bcffd4b733e166668/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d6e6575726f2d636c7573746572696e672d736f6d2e706e67" width="400" alt="Color clustering using Self-Organizing Map Networks" data-canonical-src="http://accord-framework.net/images/samples/accord-neuro-clustering-som.png"></a>
<br><sub>Color clustering using Self-Organizing Map Networks.</sub></p>
</div>
            <div id="text-45" class="hidden">
<ul>
<li>Download the <a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLY15YtTWM71tovBGA">application</a>
</li>
<li>Browse the <a href="https://github.com/accord-net/framework/tree/development/Samples/Neuro/Travelling%20Salesman%20(SOM)">source code</a>
</li>
</ul>
<p><a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLY15YtTWM71tovBGA"><img src="https://camo.githubusercontent.com/e7cc7c38069e85671267a10f6aebd9885bd3a77a/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d6e6575726f2d74726176656c6c696e672d73616c65736d616e2e706e67" width="400" alt="Delta rule learning, perceptron learning and multilayer perceptron learning" data-canonical-src="http://accord-framework.net/images/samples/accord-neuro-travelling-salesman.png"></a>
<br><sub>Delta rule learning, perceptron learning and multilayer perceptron learning.</sub></p>
</div>
            <div id="text-46" class="hidden">
<ul>
<li>Download the <a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLY_tGkhqggSarsjgQ">application</a>
</li>
<li>Browse the <a href="https://github.com/accord-net/framework/tree/master/Samples/Statistics/Handwriting%20(KDA)">source code</a>
</li>
</ul>
<p>This sample application shows how to perform handwritten digit recognition using <a href="http://accord-framework.net/docs/html/T_Accord_Statistics_Analysis_KernelDiscriminantAnalysis.htm">Kernel Discriminant Analysis</a> and <a href="http://accord-framework.net/docs/html/T_Accord_Statistics_Analysis_LinearDiscriminantAnalysis.htm">Linear Discriminant Analysis</a>.</p>
<p><a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLY_tGkhqggSarsjgQ"><img src="https://camo.githubusercontent.com/183c9fdb2a053354fb07267aa3073686f8572e5e/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d737461746973746963732d68616e6477726974696e672d286b6461292d302e706e67" alt="Recognition of handwritten digits with KDA" data-canonical-src="http://accord-framework.net/images/samples/accord-statistics-handwriting-(kda)-0.png"></a></p>
<p><a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLY_tGkhqggSarsjgQ"><img src="https://camo.githubusercontent.com/ef27af8690761b8c904c65af8011deb5a03e0b3d/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d737461746973746963732d68616e6477726974696e672d286b6461292d312e706e67" alt="Recognition of handwritten digits with KDA" data-canonical-src="http://accord-framework.net/images/samples/accord-statistics-handwriting-(kda)-1.png"></a></p>
<p><a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLY_tGkhqggSarsjgQ"><img src="https://camo.githubusercontent.com/3797f3d8b32a970e0381428e9242d0ccfe5c5bc2/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d737461746973746963732d68616e6477726974696e672d286b6461292d322e706e67" alt="Recognition of handwritten digits with KDA" data-canonical-src="http://accord-framework.net/images/samples/accord-statistics-handwriting-(kda)-2.png"></a></p>
<p><sub>Handwritten digits with KDA sample application</sub></p>
</div>
            <div id="text-47" class="hidden">
<ul>
<li>Download the <a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLY8AnTDcVBnLnLHag">application</a>
</li>
<li>Browse the <a href="https://github.com/accord-net/framework/tree/master/Samples/Statistics/Filters">source code</a>
</li>
</ul>
<p><a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLY8AnTDcVBnLnLHag"><img src="https://camo.githubusercontent.com/e98549e7763b962148d144a824468a36c51f04cb/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d737461746973746963732d66696c746572732e706e67" alt="DataTable filters" data-canonical-src="http://accord-framework.net/images/samples/accord-statistics-filters.png"></a></p>
</div>
            <div id="text-48" class="hidden">
<ul>
<li>Download the <a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLZC3Mdrt-8qhaJPjw">application</a>
</li>
<li>Browse the <a href="https://github.com/accord-net/framework/tree/master/Samples/Statistics/Gestures%20(HMMs)">source code</a>
</li>
</ul>
<p>Demonstrates how to use Hidden Markov Models (HMMs) and Accord.NET Markov Sequence
Classifiers to recognize sequences of discrete observations.</p>
<p><a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLZC3Mdrt-8qhaJPjw"><img src="https://camo.githubusercontent.com/c7b356fa72070aba484234d28ebc076daeba91f9/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d737461746973746963732d73657175656e6365732d28686d6d292e706e67" alt="Hidden Markov Model sample application" data-canonical-src="http://accord-framework.net/images/samples/accord-statistics-sequences-(hmm).png"></a></p>
<p><sub>Hidden Markov Model sample application</sub></p>
</div>
            <div id="text-49" class="hidden">
<ul>
<li>Download the <a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLY2JM9JSWcFWbKSuQ">application</a>
</li>
<li>Browse the <a href="https://github.com/accord-net/framework/tree/master/Samples/Statistics/Analysis%20(KDA)">source code</a>
</li>
</ul>
<p>Sample application demonstrating how to use Kernel Discriminant Analysis (also known
as KDA, or <em>Non-linear (Multiple) Discriminant Analysis using Kernels</em>) 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.</p>
<p><a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLY2JM9JSWcFWbKSuQ"><img src="https://camo.githubusercontent.com/f2ccde0e7b912859bea9b8fafb51e8427817ce30/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d737461746973746963732d616e616c797369732d286b6461292e706e67" alt="Kernel discriminant analysis" data-canonical-src="http://accord-framework.net/images/samples/accord-statistics-analysis-(kda).png"></a></p>
<p><sub>Kernel discriminant analysis</sub></p>
</div>
            <div id="text-50" class="hidden">
<ul>
<li>Download the <a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLY3QxDZK75Ar1pyyw">application</a>
</li>
<li>Browse the <a href="https://github.com/accord-net/framework/tree/master/Samples/Statistics/Analysis%20(KPCA)">source code</a>
</li>
</ul>
<p>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.</p>
<p><a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLY3QxDZK75Ar1pyyw"><img src="https://camo.githubusercontent.com/cd4f4da29d7e78f8534bc5b02f32ab5c72689ec1/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d737461746973746963732d616e616c797369732d28706361292e706e67" alt="Kernel principal component analysis" data-canonical-src="http://accord-framework.net/images/samples/accord-statistics-analysis-(pca).png"></a></p>
<p><sub>Kernel principal component analysis</sub></p>
</div>
            <div id="text-51" class="hidden">
<ul>
<li>Download the <a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLY4bHwbCFZtBgqr8w">application</a>
</li>
<li>Browse the <a href="https://github.com/accord-net/framework/tree/master/Samples/Statistics/Analysis%20(LDA)">source code</a>
</li>
</ul>
<p>Sample application demonstrating how to use Linear Discriminant Analysis (also known
as LDA, or <em>Fisher's (Multiple) Linear Discriminant Analysis</em>) 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</p>
<p><a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLY4bHwbCFZtBgqr8w"><img src="https://camo.githubusercontent.com/0f7e9fb3b79f9702b6d50b3812637510b4102d7a/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d737461746973746963732d616e616c797369732d286c6461292e706e67" alt="Linear discriminant analysis" data-canonical-src="http://accord-framework.net/images/samples/accord-statistics-analysis-(lda).png"></a></p>
<p><sub>Linear discriminant analysis</sub></p>
</div>
            <div id="text-52" class="hidden">
<ul>
<li>Download the <a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLY5RUcwwxBk9HHPIA">application</a>
</li>
<li>Browse the <a href="https://github.com/accord-net/framework/tree/master/Samples/Statistics/Analysis%20(PCA)">source code</a>
</li>
</ul>
<p>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</p>
<p><a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLY5RUcwwxBk9HHPIA"><img src="https://camo.githubusercontent.com/cd4f4da29d7e78f8534bc5b02f32ab5c72689ec1/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d737461746973746963732d616e616c797369732d28706361292e706e67" alt="Principal component analysis" data-canonical-src="http://accord-framework.net/images/samples/accord-statistics-analysis-(pca).png"></a></p>
<p><sub>Principal component analysis</sub></p>
</div>
            <div id="text-53" class="hidden">
<ul>
<li>Download the <a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLZFdMDabBvqidaOWA">application</a>
</li>
<li>Browse the <a href="https://github.com/accord-net/framework/tree/master/Samples/Statistics/Analysis%20(ICA)">source code</a>
</li>
</ul>
<p>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.</p>
<p><a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLZFdMDabBvqidaOWA"><img src="https://camo.githubusercontent.com/241387e93c493271429a30a6189a955514d74e56/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d737461746973746963732d736f756e642d64656d6978696e672d28696361292e706e67" alt="Independent component analysis for blind source separation" data-canonical-src="http://accord-framework.net/images/samples/accord-statistics-sound-demixing-(ica).png"></a></p>
<p><sub>Independent component analysis for blind source separation</sub></p>
</div>
            <div id="text-54" class="hidden">
<ul>
<li>Download the <a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLY6rt-8QENIBU6KCw">application</a>
</li>
<li>Browse the <a href="https://github.com/accord-net/framework/tree/master/Samples/Statistics/Analysis%20(PLS)">source code</a>
</li>
</ul>
<p>Demonstrates how to use Partial Least Squares to fit a (multiple and multivariate)
linear regression model from high-dimensionality data.</p>
<p><a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLY6rt-8QENIBU6KCw"><img src="https://camo.githubusercontent.com/ed2c22a0ed42de9486f450bba3ec40c63b51f111/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d737461746973746963732d616e616c797369732d28706c73292e706e67" alt="Partial Least Squares" data-canonical-src="http://accord-framework.net/images/samples/accord-statistics-analysis-(pls).png"></a></p>
<p><sub>Partial least squares analysis</sub></p>
</div>
            <div id="text-55" class="hidden">
<ul>
<li>Download the <a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLZBWlEVmZcg_P2m4w">application</a>
</li>
<li>Browse the <a href="https://github.com/accord-net/framework/tree/master/Samples/Statistics/Regression%20(Linear%20and%20Logistic)">source code</a>
</li>
</ul>
<p>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.</p>
<p><a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLZBWlEVmZcg_P2m4w"><img src="https://camo.githubusercontent.com/547435e31134c5586275918386d32d1a5846b2d9/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d737461746973746963732d72656772657373696f6e2d286c696e6561722d616e642d6c6f676973746963292e706e67" alt="Linear and logistic regression" data-canonical-src="http://accord-framework.net/images/samples/accord-statistics-regression-(linear-and-logistic).png"></a></p>
</div>
            <div id="text-56" class="hidden">
<ul>
<li>Download the <a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLY7BDojROGrVjk1Hg">application</a>
</li>
<li>Browse the <a href="https://github.com/accord-net/framework/tree/master/Samples/Statistics/Classification%20(MLR)">source code</a>
</li>
</ul>
<p>Sample application demonstrating how to classify objects into different classes using Multinomial Logistic Regression Analsysis. Performs chi-square
tests and computes Wald's statistics for the logistic regression coefficients.</p>
<p><a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLY7BDojROGrVjk1Hg"><img src="https://camo.githubusercontent.com/09ac77591c0f90018d802589e816181a5cd307ae/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d737461746973746963732d636c617373696669636174696f6e2d286d6c72292e706e67" alt="Multinomial Logistic Regression Analysis" data-canonical-src="http://accord-framework.net/images/samples/accord-statistics-classification-(mlr).png"></a></p>
</div>
            <div id="text-57" class="hidden">
<ul>
<li>Download the <a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLY-usYVLQU7pmVfPw">application</a>
</li>
<li>Browse the <a href="https://github.com/accord-net/framework/tree/master/Samples/Statistics/Eigenfaces%20(PCA)">source code</a>
</li>
</ul>
<p>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.</p>
<p><a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLY-usYVLQU7pmVfPw"><img src="https://camo.githubusercontent.com/811b18425687d62dfd54b7c6361b332aa925bf22/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d737461746973746963732d656967656e66616365732d28706361292e706e67" alt="Eigenfaces" data-canonical-src="http://accord-framework.net/images/samples/accord-statistics-eigenfaces-(pca).png"></a></p>
<p><sub>Principal component analysis</sub></p>
</div>
            <div id="text-58" class="hidden">
<ul>
<li>Download the <a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLZA6LPMYBGyRFEUzQ">application</a>
</li>
<li>Browse the <a href="https://github.com/accord-net/framework/tree/master/Samples/Statistics/Performance%20(ROC)">source code</a>
</li>
</ul>
<p>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.</p>
<p><a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLZA6LPMYBGyRFEUzQ"><img src="https://camo.githubusercontent.com/547435e31134c5586275918386d32d1a5846b2d9/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d737461746973746963732d72656772657373696f6e2d286c696e6561722d616e642d6c6f676973746963292e706e67" alt="ROC curve" data-canonical-src="http://accord-framework.net/images/samples/accord-statistics-regression-(linear-and-logistic).png"></a></p>
</div>
            <div id="text-59" class="hidden">
<ul>
<li>Download the <a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLZHTVD5MBupWZTOJQ">application</a>
</li>
<li>Browse the <a href="https://github.com/accord-net/framework/tree/master/Samples/Statistics/Survival%20(Cox)">source code</a>
</li>
</ul>
<p>How to perform survival analysis using Cox's Proportional Hazards model for both
regression and prediction of time-censured data.</p>
<p><a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLZHTVD5MBupWZTOJQ"><img src="https://camo.githubusercontent.com/42fd7f345647cb93ef886f80a065d996ab35bf6f/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d737461746973746963732d737572766976616c2d28636f78292e706e67" alt="Cox Proportional Hazards" data-canonical-src="http://accord-framework.net/images/samples/accord-statistics-survival-(cox).png"></a></p>
</div>
            <div id="text-60" class="hidden">
<ul>
<li>Download the <a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLY9WJeA1E3rQt2ZyQ">application</a>
</li>
<li>Browse the <a href="https://github.com/accord-net/framework/tree/master/Samples/Statistics/Gestures%20(HMMs)">source code</a>
</li>
</ul>
<p>Learning and recognition of mouse gestures using hidden Markov model-based classifiers and Hidden Conditional Random Fields. Showcased in a series of CodeProject articles under the name <em>Sequence Classifiers in C#</em>.</p>
<p><a href="http://www.youtube.com/watch?v=jn6rjAf3HeQ"><img src="https://camo.githubusercontent.com/1b826831f8aa1e136c97508fabcfb49ea4633b75/687474703a2f2f696d672e796f75747562652e636f6d2f76692f6a6e36726a4166334865512f302e6a7067" alt="Mouse gesture recognition with hidden Markov models" data-canonical-src="http://img.youtube.com/vi/jn6rjAf3HeQ/0.jpg"></a></p>
<p><sub>Mouse gesture recognition with hidden Markov models.</sub></p>
</div>
            <div id="text-61" class="hidden">
<ul>
<li>Download the <a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLZGEYOPJNzLJ__7Kg">application</a>
</li>
<li>Browse the <a href="https://github.com/cesarsouza/statistics-workbench">source code</a>
</li>
</ul>
<p><a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLZGEYOPJNzLJ__7Kg"><img src="https://camo.githubusercontent.com/2fc239c6309e1956eecf1545b7eecd179ae6a9b8/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d737461746973746963732d776f726b62656e63682e706e67" width="400" alt="Statistics Workbench, a sample application demonstrating all probability distributions available in the framework" data-canonical-src="http://accord-framework.net/images/samples/accord-statistics-workbench.png"></a>
<br><sub>Statistics Workbench, a sample application demonstrating all probability distributions available in the framework.</sub></p>
</div>
            <div id="text-62" class="hidden">
<ul>
<li>Download the <a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLZdMKENtO648ET0UQ">application</a>
</li>
<li>Browse the source code</li>
</ul>
<p><a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLZdMKENtO648ET0UQ"><img src="https://camo.githubusercontent.com/741b0b52b5a3b6aa0c0a82ca4a06da89ad336595/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d756e6974792d756e6974792e706e67" width="400" alt="Sample application demonstrating how to use the Accord.NET Framework in Unity" data-canonical-src="http://accord-framework.net/images/samples/accord-unity-unity.png"></a>
<br><sub>Sample application demonstrating how to use the Accord.NET Framework in Unity.</sub></p>
</div>
            <div id="text-63" class="hidden">
<ul>
<li>Download the <a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLZRlH6wmNVruQDPqg">application</a>
</li>
<li>Browse the <a href="https://github.com/accord-net/framework/tree/development/Samples/Video/Kinect%20Capture">source code</a>
</li>
</ul>
<p><a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLZRlH6wmNVruQDPqg"><img src="https://camo.githubusercontent.com/37f2902232f4462aabbafd8ea9c1466e61f66f90/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d766973696f6e2d6b696e6563742d636170747572652e706e67" width="400" alt="Kinect (v1) Capture sample application" data-canonical-src="http://accord-framework.net/images/samples/accord-vision-kinect-capture.png"></a>
<br><sub>Sample application demonstrating how to capture depth images using the first-generation Kinect.</sub></p>
</div>
            <div id="text-64" class="hidden">
<ul>
<li>Download the <a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLZTvxn2yVLHTlSQ3w">application</a>
</li>
<li>Browse the <a href="https://github.com/accord-net/framework/tree/development/Samples/Video/Snapshot%20Maker">source code</a>
</li>
</ul>
<p><a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLZTvxn2yVLHTlSQ3w"><img src="https://camo.githubusercontent.com/9fb2d0a2f409e0a688203eb6deb479fec1107a10/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d766964656f2d736e617073686f742d6d616b65722e706e67" width="400" alt="Snapshot maker sample application" data-canonical-src="http://accord-framework.net/images/samples/accord-video-snapshot-maker.png"></a>
<br><sub>Snapshot maker sample application.</sub></p>
</div>
            <div id="text-65" class="hidden">
<ul>
<li>Download the <a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLZSb4uR7x00i7dLdw">application</a>
</li>
<li>Browse the <a href="https://github.com/accord-net/framework/tree/development/Samples/Video/Two%20Cameras">source code</a>
</li>
</ul>
<p><a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLZSb4uR7x00i7dLdw"><img src="https://camo.githubusercontent.com/51dc8520e3c9e700b89c27bf7a88051a355d9e70/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d766964656f2d74776f2d63616d657261732e706e67" width="400" alt="Sample application demonstrating how to record from two cameras at the same time using Accord.NET" data-canonical-src="http://accord-framework.net/images/samples/accord-video-two-cameras.png"></a>
<br><sub>Sample application demonstrating how to record from two cameras at the same time using Accord.NET</sub></p>
</div>
            <div id="text-66" class="hidden">
<ul>
<li>Download the <a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLZUPSWSBw_HJLysKg">application</a>
</li>
<li>Browse the <a href="https://github.com/accord-net/framework/tree/development/Samples/Video/Video%20Player">source code</a>
</li>
</ul>
<p><a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLZUPSWSBw_HJLysKg"><img src="https://camo.githubusercontent.com/82e9e686f49cd2b1e3b1bb1c89bd86a5c2535ecf/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d766964656f2d766964656f2d706c617965722e706e67" width="400" alt="Sample application demonstrating how to replay video from a local capture device, a JPEG stream, a MJPEG stream, or another video source" data-canonical-src="http://accord-framework.net/images/samples/accord-video-video-player.png"></a>
<br><sub>Sample application demonstrating how to replay video from a local capture device, a JPEG stream, a MJPEG stream, or another video source.</sub></p>
</div>
            <div id="text-67" class="hidden">
<ul>
<li>Download the <a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLZVUPrHXn1t0NT56A">application</a>
</li>
<li>Browse the <a href="https://github.com/accord-net/framework/tree/development/Samples/Video/Ximea%20Sample">source code</a>
</li>
</ul>
<p><a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLZVUPrHXn1t0NT56A"><img src="https://camo.githubusercontent.com/297af26d1198424399c69aedec84c5a60df379d0/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d766964656f2d78696d65612e706e67" width="400" alt="Sample application demonstrating how to control a Ximea camera and make the camera interact with C#/.NET." data-canonical-src="http://accord-framework.net/images/samples/accord-video-ximea.png"></a>
<br><sub>Sample application demonstrating how to control a Ximea camera and make the camera interact with C#/.NET.</sub></p>
</div>
            <div id="text-68" class="hidden">
<ul>
<li>Download the <a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLZeCwJebrNetImPtw">application</a>
</li>
<li>Go to the <a href="http://cesarsouza.github.io/screencast-capture/">project's website</a>.</li>
</ul>
<p>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.</p>
<p><a href="http://www.youtube.com/watch?v=nZp0ngXb_Ro"><img src="https://camo.githubusercontent.com/43f3f86238ea4f6a38fd214edb6ae6595dcff5de/687474703a2f2f696d672e796f75747562652e636f6d2f76692f6e5a70306e6758625f526f2f302e6a7067" alt="Screencast capture lite" data-canonical-src="http://img.youtube.com/vi/nZp0ngXb_Ro/0.jpg"></a><br><sub>Screencast Capture Lite.</sub></p>
</div>
            <div id="text-69" class="hidden">
<ul>
<li>Download the <a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLZZG2yATtgpIjj_Iw">application</a>
</li>
<li>Browse the <a href="https://github.com/accord-net/framework/tree/master/Samples/Vision/Face%20detection%20(Viola-Jones)">source code</a>
</li>
</ul>
<p>Face detection using the Face detection based in Haar-like rectangular features method often known as the Viola-Jones method</p>
<p><a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLZZG2yATtgpIjj_Iw"><img src="https://camo.githubusercontent.com/da15f8332cc1f8a7f38b006f2f27a5a005160b03/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d766973696f6e2d666163652d646574656374696f6e2d2876696f6c612d6a6f6e6573292e706e67" alt="Haar cascade face detection" data-canonical-src="http://accord-framework.net/images/samples/accord-vision-face-detection-(viola-jones).png"></a></p>
<p><sub>Haar cascade face detection.</sub></p>
</div>
            <div id="text-70" class="hidden">
<ul>
<li>Download the <a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLZYaTSZLZoBAbxWOw">application</a>
</li>
<li>Browse the <a href="https://github.com/accord-net/framework/tree/master/Samples/Vision/Face%20tracking%20(Camshift)">source code</a>
</li>
</ul>
<p>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)</p>
<p><a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLZYaTSZLZoBAbxWOw"><img src="https://camo.githubusercontent.com/bf8a8353959778cd06c6033953307e421994d5b4/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d766973696f6e2d666163652d747261636b696e672d2863616d7368696674292e706e67" alt="Face tracking with the Camshift algorithm" data-canonical-src="http://accord-framework.net/images/samples/accord-vision-face-tracking-(camshift).png"></a></p>
<p><sub>Face tracking with the Camshift algorithm.</sub></p>
</div>
            <div id="text-71" class="hidden">
<ul>
<li>Download the <a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLZWsyVT2PDOD-2Obg">application</a>
</li>
<li>Browse the <a href="https://github.com/accord-net/framework/tree/master/Samples/Vision/Controller%20(interface%20control)">source code</a>
</li>
</ul>
<p>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.</p>
<p><a href="http://www.youtube.com/watch?v=tqAfqJsW2Wo"><img src="https://camo.githubusercontent.com/f011c98eca35d019928b4166dd5a69ff18f99c94/687474703a2f2f696d672e796f75747562652e636f6d2f76692f74714166714a735732576f2f302e6a7067" alt="Controlling a computer interface using computer vision" data-canonical-src="http://img.youtube.com/vi/tqAfqJsW2Wo/0.jpg"></a></p>
<p><sub>Controlling a computer interface using computer vision.</sub></p>
</div>
            <div id="text-72" class="hidden">
<ul>
<li>Download the <a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLZaGFbZx3l5O7l0KA">application</a>
</li>
<li>Browse the <a href="https://github.com/accord-net/framework/tree/master/Samples/Vision/Gloves%20(Color%20tracking)">source code</a>
</li>
</ul>
<p>How to segment and track color objects using a simple and inexpensive HSL-based color tracker.</p>
<p><a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLZaGFbZx3l5O7l0KA"><img src="https://camo.githubusercontent.com/bf8a8353959778cd06c6033953307e421994d5b4/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d766973696f6e2d666163652d747261636b696e672d2863616d7368696674292e706e67" alt="Color-based video tracking" data-canonical-src="http://accord-framework.net/images/samples/accord-vision-face-tracking-(camshift).png"></a>
<sub>Color-based video tracking.</sub></p>
</div>
            <div id="text-73" class="hidden">
<ul>
<li>Download the <a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLZX6ClUgGPQThAvmA">application</a>
</li>
<li>Browse the <a href="https://github.com/accord-net/framework/tree/development/Samples/Vision/Dynamic%20Virtual%20Wall%20(Kinect)">source code</a>
</li>
</ul>
<p><a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLZb_PZXGkO-FePLYw"><img src="https://camo.githubusercontent.com/d4718e83c84fc0bea2d8e34a232c1e12a493c10d/68747470733a2f2f75706c6f61642e77696b696d656469612e6f72672f77696b6970656469612f636f6d6d6f6e732f7468756d622f362f36372f58626f782d3336302d4b696e6563742d5374616e64616c6f6e652e706e672f3132303070782d58626f782d3336302d4b696e6563742d5374616e64616c6f6e652e706e67" width="400" alt="Dynamic virtual wall sample application" data-canonical-src="https://upload.wikimedia.org/wikipedia/commons/thumb/6/67/Xbox-360-Kinect-Standalone.png/1200px-Xbox-360-Kinect-Standalone.png"></a>
<br><sub>Dynamic virtual wall sample application.</sub></p>
</div>
            <div id="text-74" class="hidden">
<ul>
<li>Download the <a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLZb_PZXGkO-FePLYw">application</a>
</li>
<li>Browse the <a href="https://github.com/accord-net/framework/tree/development/Samples/Vision/Motion%20detection">source code</a>
</li>
</ul>
<p><a href="https://1drv.ms/u/s!AoiTwBxoR4OAoLZb_PZXGkO-FePLYw"><img src="https://camo.githubusercontent.com/622093784315f5ebf9597f322ef496f5e5aa4207/687474703a2f2f6163636f72642d6672616d65776f726b2e6e65742f696d616765732f73616d706c65732f6163636f72642d766973696f6e2d6d6f74696f6e2d6465746563746f722e706e67" width="400" alt="Motion detector sample application" data-canonical-src="http://accord-framework.net/images/samples/accord-vision-motion-detector.png"></a>
<br><sub>Motion detector sample application.</sub></p>

        </div>
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