Best Image Recognition Software

Image recognition, also known as computer vision, allows applications using specific deep learning algorithms to understand images or videos. In these scenarios, images are data in the sense that they are inputted into an algorithm, the algorithm performs a requested task, and the algorithm outputs a solution provided by the image. One common execution for computer vision applications includes facial recognition—whether for tagging friends on Facebook or a police department identifying a potential suspect—solely based on an image. Another use for image recognition is in the medical field, where artificial intelligence, using image recognition, can observe an x-ray and decipher the diagnosis solely based on the image. Some other aspects of image recognition include image restoration, object recognition, and scene reconstruction. These capabilities may be embedded inside intelligent applications or offered as deep learning algorithms inAI platforms.

To qualify for inclusion in the Image Recognition category, a product must:

  • Provide a deep learning algorithm specifically for image recognition
  • Connect with image data pools to learn a specific solution or function
  • Consume the image data as an input and provide an outputted solution

Image Recognition Software Grid® Overview

The best Image Recognition Software products are determined by customer satisfaction (based on user reviews) and market presence (based on products’ scale, focus, and influence) and placed into four categories on the Grid®:
  • Products in the Leader quadrant are rated highly by G2 Crowd users and have substantial Market Presence scores. Leaders include: Google Cloud Vision API
  • High Performers are highly rated by their users, but have not yet achieved the Market Presence of the Leaders. High Performers include: OpenCV
  • Contenders have significant Market Presence and resources, but have received below average user Satisfaction ratings or have not yet received a sufficient number of reviews to validate the solution. Contenders include: Deepdream
  • Niche solutions do not have the Market Presence of the Leaders. They may have been rated positively on customer Satisfaction, but have not yet received enough reviews to validate them.
G2 Crowd Grid® for Image Recognition
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    Image Recognition reviews by real, verified users. Find unbiased ratings on user satisfaction, features, and price based on the most reviews available anywhere.

    Derive insight from images with our powerful Cloud Vision API


    OpenCV is a tool that has has C++, C, Python and Java interfaces and supports Windows, Linux, Mac OS, iOS and Android for computational efficiency and with a strong focus on real-time applications, written in optimized C/C++, the library can take advantage of multi-core processing and enabled to take advantage of the hardware acceleration of the underlying heterogeneous compute platform


    And this is where Google's deep dream ideas originate. With simple words you give to an AI program a couple of images and let it know what those images contain ( what objects - dogs, cats, mountains, bicycles, ... ) and give it a random image and ask it what objects it can find in this image.


    Microsoft Computer Vision API is a cloud-based API tool that provides developers with access to advanced algorithms for processing images and returning informatio, by uploading an image or specifying an image URL, it analyze visual content in different ways based on inputs and user choices.


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


    DeepPy is a MIT licensed deep learning framework that tries to add a touch of zen to deep learning as it allows for Pythonic programming based on NumPy's ndarray,has a small and easily extensible codebase, runs on CPU or Nvidia GPUs and implements the following network architectures feedforward networks, convnets, siamese networks and autoencoders.


    scikit-image is a collection of algorithms for image processing.


    Clarifai offers a suite of tools that make it easy for anyone to quickly and accurately train, customize, and use machine learning-powered image and video recognition in their products.


    Microsoft Face API is a cloud-based service that provides the advanced face algorithms with two main functions: face detection with attributes and face recognition to allow user detect, identify, analyze, organize and tag faces in photos.


    Amazon Rekognition makes it easy to add image and video analysis to your applications. It can identify the objects, people, text, scenes, and activities, or any inappropriate content from an image or video.


    Azure Custom Vision Service is a tool for building custom image classifiers, and for making them better over time. This service enables you to identify your own objects and things in images.


    Azure Face API uses state-of-the-art cloud-based face algorithms to detect and recognize human faces in images.Its capabilities include features like face detection, face verification, and face grouping to organize faces into groups based on their visual similarity.


    Azure Video Indexer enables customers with digital video and audio content to automatically extract metadata and use it to build intelligent innovative applications.


    Caffe is a deep learning framework made with expression, speed, and modularity in mind.


    Gesture Recognition Toolkit (GRT) is a cross-platform, open-source, C++ machine learning library designed for real-time gesture recognition.


    IBM Watson Visual Recognition is a tool that allow users to automatically identify subjects and objects contained within the image and organize and classify these images into logical categories.


    Microsoft Emotion API is a tool that analyze faces to detect a range of feelings and personalize your app's responses.


    Microsoft Video API is a cloud-based API that provides advanced algorithms for tracking faces, detecting motion, stabilizing and creating thumbnails from video, it allows user to build more personalized and intelligent apps by understanding and automatically transforming video content.


    VIGRA is a computer vision library that puts its main emphasis on flexible algorithms.


    3VR is a video technology and data company that solves the challenges associated with video searchability, allowing customers to rapidly gather real-time intelligence from the unstructured video data that is produced by a single camera or a global network of cameras.


    ACTi video analytics are designed to help you transform your video surveillance network into a smart detection system and a valuable resource for business management.


    AForge.Vision is a vision library that contains some vision classes - set of motion detection algorithms.


    Captricity’s AI-powered automation enables paper to travel at the speed of digital. Captricity is used by eight of the top ten U.S. insurance companies and other enterprises to extract and enhance data from any customer channel—including handwritten documents—and deliver it seamlessly into downstream business systems.


    CCV is a open source/cross-platform solution for blob tracking with computer vision. that can interface with various web cameras and video devices as well as connect to various TUIO/OSC/XML enabled applications and supports many multi-touch lighting techniques including: FTIR, DI, DSI, and LLP with expansion planned for the future vision applications (custom modules/filters).


    DeepLearningKit is an open source Deep Learning Framework for Apple's iOS, OS X & tvOS. Developed in Swift & Metal (GPU acceleration).


    Image Processing is a tool that deals with array2d objects that contain various kinds of pixels or user defined generic image objects.


    Eblearn is an object-oriented C++ library that implements various machine learning models, including energy-based learning, gradient-based learning for machine composed of multiple heterogeneous modules.


    Hive is a complete deeplearning toolkit that applies visual intelligence to difficult problems, changing the way businesses analyze unstructured visual data.


    IBM Intelligent Video Analytics helps security and public safety organizations develop comprehensive security, intelligence and investigative capabilities using video.


    Imagga is an image recognition platform-as-a-service that provides image tagging APIs for developers and businesses to build scalable, image and video intensive apps.


    MetaEyes is a reporting service that with the help of image recognition technology analyzes Instagram (plus other services) photos, revealing a wealth of actionable information. MetaEyes can detect: • Faces & Demographics • Explicit/Racy Content • Sentiment • Scenes & Objects • Logos • Locations • Celebrities • Other info Powerful reporting tools to analyze and filter data in myriads of ways. • Summary and granular reports • Filter detailed reports by all MetaEyes attributes • View by date range and different sorting methods • Export as PDF or CSV files for further analysis Photos provide a far more refined insight allowing for novel ways of engagement. •Discover potential fans by attributes previously undetectable • Surface and engage with previously ghost influencers • Find user-generated brand photos and directly contact to use in marketing campaigns • Monitor for brand related crisis situations and engage directly to avoid negative virality


    MobileEngine makes it easy for you to add image recognition to your app. You provide a reference database of images (e.g. artwork, consumer packaged goods, book covers, catalog pages, etc.) and when your users photograph that object, MobileEngine finds your matching reference image.


    MXNet is a Flexible and Efficient Library for Deep Learning that supports both imperative and symbolic programming, calculates the gradient automatically for training a model, runs on CPUs or GPUs, on clusters, servers, desktops, or mobile phones and supports distributed training on multiple CPU/GPU machines, including AWS, GCE, Azure, and Yarn clusters.


    OpenFace is a Python and Torch implementation of face recognition with deep neural networks and is based on the CVPR 2015


    PCV is an open source Python module for computer vision


    Torch is a scientific computing framework with wide support for machine learning algorithms that puts GPUs first.


    Vize.ai AI is custom image recognition and classification API, designed to allow developers and businesses to analyze image data.


    Vize is a custom image recognition API solution.


    VLFeat is an open source library that implements popular computer vision algorithms specializing in image understanding and local features extraction and matching, it include Fisher Vector, VLAD, SIFT, MSER, k-means, hierarchical k-means, agglomerative information bottleneck, SLIC superpixels, quick shift superpixels, large scale SVM training, and many others. It is written in C for efficiency and compatibility, with interfaces in MATLAB for ease of use, and detailed documentation throughout. It supports Windows, Mac OS X, and Linux.


    WineEngine is powered by TinEye's unparalleled image recognition technology and has been engineered and optimized to work with photographs captured by users' smart devices.


    Mobile app enabling retailers to achieve retail excellence through better in-store execution startup retailtech saas technology retail


    Kate from G2 Crowd

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