AForge.NET Framework v.2.2.4


AForge.NET is an open source C# framework designed for developers and researchers in the fields of Computer Vision and Artificial Intelligence - image processing, neural networks, genetic algorithms, fuzzy logic, machine learning, robotics, etc.The framework is comprised by the set of libraries and sample applications, which demonstrate their features: * AForge.Imaging - library with image processing routines and filters; * AForge.Vision - computer vision library; * AForge.Video - set of libraries for video processing; * AForge.Neuro - neural networks computation library; * AForge.Genetic - evolution programming library; * AForge.Fuzzy - fuzzy computations library; * AForge.Robotics - library providing support of some robotics kits; * AForge.MachineLearning - machine learning library; * etc.The work on the framework's improvement is in constants progress, what means that new feature and namespaces are coming constantly. To get knowledge about its progress you may track source repository's log or visit project discussion group to get the latest information about it.The framework is provided not only with different libraries and their sources, but with many sample applications, which demonstrate the use of this framework, and with documentation help files, which are provided in HTML Help format. The documentation is also available on-line.In the case you have found an issue in any component of the framework or you would like to request for a new feature, you may feel free to submit an issue/request in the issues tracking system.In case you are interested in the project and would like to learn more about it or in case you would like to contribute it, you are more than welcome to participate in the project's discussion group.AForge.NET framework consists of several libraries, so below the framework's features are presented grouped by them:AForge.Imaging, which is the biggest library of the framework so far, contains different image processing routines, which are aimed to help as in image enhancement/processing, as in some computer vision tasks: * Linear color correction filters (RGB/HSL/YCbCr correction, brightness/contrast/saturation correction); * Nonlinear color correction filters (contrast stretch, histogram equalization, color remapping, gamma correction); * Image re-coloring filters (grayscale, sepia, hue modifier, rotate channels, invert); * Pixel filtering by color (RGB, HSL, YCbCr color spaces); * Color channels manipulations (RGB and YCbCr color spaces); * Binarization filters (threshold, threshold with carry, ordered dithering, Bayer dithering, Floyd-Steinberg dithering, Burkes dithering, Jarvis-Judice-Ninke dithering, Sierra dithering, Stucki dithering); * Adaptive binarization (simple image statistics, iterative thresholding, Otsu thresholding); * Convolution filters (mean, blur, sharpen, edges, Gaussian blur, custom convolution filters); * Mathematical morphology filters (erosion, dilatation, opening, closing, top hat, bottom hat, hit-and-miss); * Edge detectors (homogeneity, difference, sobel, canny); * 2 source filters (merge, intersect, add, subtract, difference, move towards, morph); * Blobs processing (counting, extraction, filtering, connected component labeling); * Corner detectors (Moravec, Susan); * Quadrilateral transformation and corners' finding; * Resize and rotation (nearest neighbor, bilinear, bicubic); * Hough transformation (line and circle transformations); * Exhaustive template and block matchin; * Image color statistics (RGB, HSL, YCbCr) and vertical/horizontal statistics (RGB); * Smoothing filters (Median, Mean, Conservative Smoothing, Adaptive Smoothing); * Texture generators (clouds, marble, wood, labyrinth, textile); * Texture filters (texturing, merging, filtering); * More effects, like pixelating, jittering, oil painting, water wave, image warping, etc; * Noise generators (additive, salt-and-papper); * Document skew checker for checking rotation of scanned documents; * Stereo anaglyph image creating; * Flood fill filters (using specified color or calculate mean color of the area); * Flat Field Illumination correction, Simple skeletonization, Shrink, Canvas crop/fill/move, mirroring; * Fourier transformation (low-pass and hi-pass filters); * Some image decoders for custom image formats (PNM, FITS); * etc.AForge.Vision library consists of different motion detection and motion processing routines.AForge.Video library contains different classes, which provide access to video data. Nice to have it taking into account the amount of image processing stuff in the framework. * Access to JPEG and MJPEG streams, which enables access to IP cameras; * Access to USB web cameras, capture devices and video files through DirectShow interface; * Reading/writing AVI files using Audio for Windows interface.AForge.Robotics library contains some classes to manipulate some robotics kits: * Lego Mindstorm RCX Robotics kit; * Lego Mindstorm NXT Robotics kit;. * Qwerk robotics board; * Surveyor SRV-1 Blackfin robot; * Surveyor Stereo Vision System robotics board.AForge.Neuro library consists of some common neural network architectures' implementations and their learning algorithms: * Multi-layer feed forward networks utilizing activation function; * Distance networks (Kohonen SOM, for example); * Simple perceptron's learning, Delta rule learning, Back Propagation learning, Kohonen SOM learning, Evolutionary learning based on Genetic Algorithm; * Activation functions (threshold, sigmoid, bipolar sigmoid).AForge.Genetic library consists of classes aimed to solve different tasks from Genetic Algorithms (GA), Genetic Programming (GP) and Gene Expression Programming (GEP) areas: * GA chromosomes (binary, short array, double array), GP tree based chromosome and GEP chromosome; * Selection algorithms (elite, roulette wheel, rank); * Common fitness functions (1/2D function optimization, symbolic regression, time series prediction). * Population class to handle chromosomes.AForge.Fuzzy library consists of classes to perform different fuzzy computations, starting from using basic fuzzy sets and linguistic variables and continuing with complete inference system, which is capable of running set of fuzzy rules evaluating requested fuzzy variable.AForge.MachineLearning library contains some classes from machine learning area: * QLearning and Sarsa learning algorithms; * Epsilon greedy, Boltzmann, Roulette wheel and Tabu Search exploration policies.The AForge.NET framework contains also some more libraries/namespaces providing additional functionality, which is used by the framework, its samples or may be used directly in applications.

AForge.NET is an open source C# framework ...

library, filters, image, framework, learning, color, processing, dithering, libraries

  • AForge.NET Framework
  • 2.2.4
  • Andrew Kirillov
  • WinXP, Win2003, Win2000, Win Vista, Windows 7
  • Shareware
  • 31.68 Mb
  • 404

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