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How To Build A Career in Computer Vision


How To Build A Career in Computer Vision


It is one of the most in-demand job titles, perched at the Number 3 spot of Indeed 2018 list of Best Jobs in US. With the rapid flow of investments in AI technologies — both at the startup level as well as within some of the world’s leading technology companies — techies and engineers can restart their career with computer vision.



Some of the reasons behind the exponential growth of computer vision technology are:

(a) hardware advancements in terms of availability of GPUs

(b) emergence of deep learning, which has changed our way of performing tasks such as image classification

(c) the availability of large datasets such as ImageNet and Caltech 101 that enables beginners and advanced practitioners to work on computer vision applications.

In terms of M&A, Intel’s acquisition of Mobileye last year in March indicates the incredible growth in this computer vision field. Another big announcement in computer vision acquisitions comes from lifestyle behemoth Nike which snapped up Tel Aviv-based company Invertex Ltd. Adam Sussman, Nike chief digital officer, had said, “The acquisition of Invertex will deepen our bench of digital talent and further our capabilities in computer vision and artificial intelligence as we create the most compelling Nike consumer experience at every touch point.”  

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How Can One Start A Career In Computer Vision?

  1. Whether you are a beginner or at an intermediate level, the best place to gain practical knowledge about algorithms and computer vision application programming is with OpenCV — an open source computer vision and machine learning software library. OpenCV is an open source software library/toolbox with APIs in other programming languages and it has a bunch of tools that are state-of-the-art in image processing and computer vision.
  2. You should gain practical experience by working through the numerous projects available on the OpenCV repository.
  3. For those who are good at Python, OpenCV tutorials would definitely come in handy. Also, Python has a lot of machine learning libraries like TensorFlow, which would be extremely useful if you wish to implement a convolutional neural network.
  4. While you can install OpenCV the open source software, some beginners prefer MATLAB as well. Check out this link for MATLAB tutorials.
  5. On the theoretical side, brush up your knowledge of some of the most common computer vision problems and their solutions, such as facial recognition through Principal Component Analysis, or object detection through Histogram of Oriented Gradients. Here are a few books available online worth a read: Computer Vision : A Modern Approach by David A Forsyth and Jean Ponce; and Multiple View Geometry in Computer Vision by Richard Hartley and Andrew Zisserman.
  6. In terms of online courses, University of Central Florida’s video lectures by Dr Mubarak Shah are widely cited. There is also an online course available from Professor Jitendra Malik of University of California, six of his papers received a thousand citations and his research group has worked on concepts such as computational modeling of human vision and analysis of biological images.
  7. According to Prof Devi Parekh of Georgia Institute of Technology, it is best to pick up a project or an application. One can ideally start by researching papers, understanding the concept used to solve the problem. She advises learners to select a problem, a dataset, as well as a library they might want to use and get become familiar with computer vision problems.
  8. While there are plenty of blogs out there on CV, PyImage Search has been voted as the best blog by CV practitioners as the best resource to learn computer vision and OpenCV

Popular Image Classification Databases To Train Image Classification/Recognition Models

Here are some of the most popular datasets available online:

  • Kaggle CIFAR-10 one of the most popular datasets consists of 60,000 color images and 10 object categories
  • Open Images – This dataset contains 9 million images, 5,000 object categories
  • ImageNet – An extremely useful resource, this database consists of 15 million images, 22,000 object categories.
  • CALTECH-101 – 9,000 images with 101 object categories.
  • Common Objects in Context (COCO) – Better known as COCO, this dataset consists of 330K images, 80 object categories.
  • PASCAL VOC Dataset – This dataset provides standardised image data sets for object class recognition

Outlook

Computer Vision is a fascinating field and many Indian e-commerce companies and startups are leveraging the application. Also a lot of Indian startups such as Sigtuple, SensoVision Systems have CV at the core.



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