Skills Needed For A Career In Computer Vision

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You are ready to delve into the world of automation and have chosen the path of a Computer Vision Engineer. This exciting role consists of learning highly advanced technologies, something today's employers are looking for.

It's a career path that will lead you to work across multiple industries. With the market for computer vision anticipated to rise from US$10.9 billion in 2019 to US$17.4 billion by 2024, it is certainly a promising future proof career.

What is a Computer Vision Engineer?

Computer vision relates to computers not only 'seeing' images but also making some sort of sense from those images, such as determining distances and movements. This technology may be used in medicine, defence, manufacturing and various types of monitoring.

A computer vision engineer often applies computer vision research that is based on a large sum of data to solve real-world problems. They spend much of their time researching and implementing machine learning primitives and computer vision for their client companies.

They work closely with other personnel to facilitate the implementation of novel embedded architectures. Computer vision engineers have a significant amount of experience with a variety of systems, such as image recognition, machine learning and segmentation.

Skills needed for a career in Computer Vision

The skills needed for computer vision jobs can be divided into three categories:

  1. Computer Vision knowledge:

    The basics of computer vision are built upon Digital Image Processing (DIP). So, you need to learn the basics of the DIP first. Then you can move forward to read computer vision topics like pattern recognition and 3D geometry. You need to know linear algebra to be able to fully understand some concepts of the computer vision like dimensionality reduction. After understanding the fundamentals of computer vision, you should also build your knowledge in deep learning, especially in Convolutional Neural Networks (CNNs).
  2. Programming:

    Python will be enough for design and prototyping. However, if you want to do some embedded work, you should also be familiar with C++.
  3. CV tools:

    OpenCV is the main tool for computer vision; You have to know it. You will never be able to know all its functions, but you should know what tools are available in it and how you can use them. Another tool that you need to be familiar with is a Deep Learning framework. You can start with Keras, which is the easiest to learn and then learn either Tensorflow or PyTorch.

If you're looking for your next Computer Vision role check out our latest vacancies or upload your Resume today.

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