Python

This python package implements Spatial Pyramid Matching to recognize images under different categories. Furthermore, Earth Mover's distance is also incorporated to better align parts of images when calculating image-to-image distance.

2. SimpleCV

SimpleCV is an open source framework for building computer vision applications. With it, you get access to several high-powered computer vision libraries such as OpenCV – without having to first learn about bit depths, file formats, color spaces, buffer management, eigenvalues, or matrix versus bitmap storage.

3. nupic.vision

This tool kit is intended to help people who are interested in using Numenta's nupic on vision related problems. There are several examples of how this tool kit can be used in the nupic.vision directory.

4. FaceDetectionCrop

This is a python script to crop images based facial recognition. It has two options, create from a webcam or from a saved image (jpg or png). OpenCV is being used to detect the facial region and PIL to do the cropping.





Haskell

1. easyVision

Haskell packages for computer vision, image processing, and pattern recognition.

Ruby

Image-recognition was developed in ruby. It compares all the input images with the target image using

L1 Distance

and

Chi-Square Statistic

.

Java

Image recognition implementation in Android, using OpenCV.

2. Moodstocks

Moodstocks library provides supersonic image recognition for mobile app. Its commercial library.

3. face-recognition

Deprecated. Face recognition Android application. Using Android SDK, OpenCV and Facebook SDK. Loading the user's Facebook pictures, scanning pictures for facial features and comparing faces to image repository for matches.





Javascript

Trains a Hopfield recurrent neural network to recognize colors after which it interprets all PNG images in the input folder and saves the results to the output folder.





CPP

1. OpenCV

OpenCV is written in C++ and its primary interface is in C++, but it still retains a less comprehensive though extensive older C interface, for documentation.

2.DeepBeliefSDK

This is a framework implements the convolutional neural network architecture .The processing code has been highly optimized to run within the memory and processing constraints of modern mobile devices. It's also easy to use together with OpenCV. The SDK for Jetpac's iOS, Android, Linux, and OS X Deep Belief image recognition framework.

3. Hearthstone-Image-Recognition

The twitch.tv bot aims to support a Hearthstone streamer in various ways by applying several image recognition algorithms. There are three main features: 1. An IRC bot to take commands from the twitch chat and return appropriate responses 2. Automatic decklist creation from arena by observing the picks 3. Automatic game state detection to inform another bot (Valuebot, Hidbot, ..) when a game is over and what the outcome was by sending "!score" commands.

4. live-gesture-recognition

live-gesture-recognition library allows individual to use Webcam to recognize gestures and perform various tasks such as opening a folder or file, loading the start menu, etc.

5. pastec

Pastec is an open source index and search engine for image recognition based on OpenCV. It can recognize flat objects such as covers, packaged goods or artworks. It has however not been designed to recognize faces, 3D objects, barcodes and QR codes.





C

1. CCV

Core Computer Vision (CCV) now runs on Mac OSX, Linux, FreeBSD, Windows, iPhone, iPad, Android, Raspberry Pi. In fact, anything that has a proper C compiler probably can run ccv. The majority of ccv will just work with no compilation flags or dependencies with the exception of convolutional networks, which requires a BLAS library.

2. MegaWave 2

MegaWave2 is a free software intended for image processing. It is made of C library of modules, that contains original algorithms written by researchers and a Unix/Linux package designed for the fast development of new image processing algorithms.

3. visionbase

Visionbase is a clean C implementation of lots image processing and recognition algorithms.

4. Astrometry.net

Automatic recognition of astronomical images or standards-compliant astrometric metadata from data.



