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Top 5 Most Popular Computer Vision Tools


  1. Computer vision is an interdisciplinary scientific field that deals with how computers can gain high-level understanding from digital images or videos.
  2. Computer vision tasks include methods for acquiring, processing, analyzing and understanding digital images, and extraction of high-dimensional data from the real world to produce numerical or symbolic information, e.g., in the form of decisions.
  3. The image data can take many forms, such as video sequences, views from multiple cameras, multi-dimensional data from a 3D scanner, or medical scanning device.
  4. Sub-domains of computer vision include scene reconstruction, event detection, video tracking, object recognition, 3D pose estimation, learning, indexing, motion estimation, visual serving, 3D scene modeling, and image restoration.
  5. Computer vision is used across industries to enhance the consumer experience, reduce costs, and increase security.

Market Size

According to Gartner, “The global computer vision market size was valued at USD 10.6 billion in 2019 and is expected to grow at a compound annual growth rate (CAGR) of 7.6% from 2020 to 2027.” Vision Intelligence will play a key role in the Post-COVID industrial world.

The global computer vision market


computer vision market

Top 5 Computer Vision Tools


  • OpenCV is a library of programming functions mainly aimed at real-time computer vision.
  • OpenCV was built to provide a common infrastructure for computer vision applications and to accelerate the use of machine perception in commercial products.
  • The library has more than 2500 optimized algorithms, which includes a comprehensive set of both classic and state-of-the-art computer vision and machine learning algorithms.
  • It has C++, Python, Java, and MATLAB interfaces and supports Windows, Linux, Android, and Mac OS.
  • OpenCV has more than 47 thousand people in the user community and an estimated number of downloads exceeding 18 million.


  • TensorFlow is a free and open-source software library for machine learning. It is an end-to-end open-source platform for machine learning.
  • It has a comprehensive, flexible ecosystem of tools, libraries, and community resources that lets researchers push the state-of-the-art in Machine Learning and developers easily build and deploy ML-powered applications.
  • TensorFlow AI Service Partners offer a range of consulting and software solutions to help you innovate faster, solve smarter, and scale bigger with TensorFlow.
  • The TensorFlow community is an active group of developers, researchers, visionaries, thinkers, and problem solvers. The door is always open to contribute, collaborate and share your ideas.


  • MATLAB allows matrix manipulations, plotting of functions and data, implementation of algorithms, creation of user interfaces, and interfacing with programs written in other languages.
  • It is a programming and numeric computing platform used by millions of engineers and scientists to analyze data, develop algorithms, and create models.
  • It includes the Live Editor for creating scripts that combine code, output, and formatted text in an executable notebook.
  • MATLAB apps let you see how different algorithms work with your data. Iterate until you have got the results you want, then automatically generate a MATLAB program to reproduce or automate your work.


  • SimpleCV is an open-source framework for building computer vision applications – meaning that it is a collection of libraries and software that you can use to develop 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.
  • It helps you build software to make your various technologies not only see the world but understand it too.
  • It is written in Python and runs on Mac, Windows, and Ubuntu Linux.


  • CUDA is a parallel computing platform and application programming interface model created by Nvidia.
  • It allows software developers and software engineers to use a CUDA-enabled graphics processing unit for general-purpose processing – an approach termed GPGPU.
  • The Toolkit from NVIDIA provides everything you need to develop GPU-accelerated applications. The Toolkit includes GPU-accelerated libraries, a compiler, development tools, and the CUDA runtime.
  • When using CUDA, developers program in popular languages such as C, C++, Fortran, Python and MATLAB and express parallelism through extensions in the form of a few basic keywords.


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