Data Science

Using Machine Learning for Green Screen Matting

AI-Generated Summary

  • A Danish startup called CloudCutout has trained neural networks to automatically isolate images from backgrounds such as green screens, blue screens, standard school photography backgrounds, and white product-shot backgrounds.
  • The service targets school photography first, aiming to charge half the standard bulk knockout cost of 50 cents per image.
  • To scale the cutout engine, CloudCutout distributes computations on IBM SoftLayer bare metal nodes equipped with NVIDIA Tesla K80 GPUs while hosting remaining infrastructure on Amazon Web Services.

Next Step

  • Read the TechCrunch article for more details on CloudCutout's machine learning approach to image editing.
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A Danish startup has trained neural networks to recognize the pattern of a perfect cut-out. CloudCutout is applying machine learning techniques to the quotidian yet persnickety process of isolating an image from a background. Their initial target market is school photography, with the aim of undercutting the market by charging half the “standard knockout” bulk cost (50 cents) per image.



The Copenhagen-based team says its algorithm can automatically cut out studio images from consistent backgrounds, such as green- or blue-screens, but also from “standard backgrounds” used for school photographs, and also from the white backgrounds popular for product shots—saving time and money. Or that’s the pitch.

To ensure that they can scale to accommodate demand for the cutout service, they’re distributing their cutout engine on IBM SoftLayer and Amazon Web Services. “Computations related to neural networks are executed on bare metal SoftLayer nodes, that provide the most recent NVIDIA Tesla K80 GPUs, whereas remaining parts of our infrastructure are hosted on AWS,” they note.

Read more on TechCrunch >>

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