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Computer Vision, Image Processing, Immersive Technology, and Computational Photography

The Lunar Eclipse, October 2014
(15min progression of the Lunar Eclipse, San Francisco/Bay Area, California, October, 2014 - Jeff M. Wang)


- An Increasingly Virtual World

[Facebook Research]: "Sharing engaging and immersive visual content such as photos, videos, 360s, and real-time augmented experiences is central to keeping in touch and building community. At Facebook, we are developing and perfecting advanced real-time computational photography and image understanding techniques that let us enhance our images and videos, track and augment faces, bodies, and the 3D world, and capture and share the 3D world with high fidelity. Our research scientists and engineers encompass a myriad of disciplines, including computer vision, computer graphics, computational photography, machine learning, interactive techniques, and mobile development."


- The Rise of Computer Vision

To a computer, the image above - like all images - is an array of pixels, numerical values that represent shades of red, green, and blue. One of the challenges computer scientists have grappled with since the 1950s has been to create machines that can make sense of photos and videos like humans do. The field of computer vision has become one of the hottest areas of research in computer science and artificial intelligence (AI). Decades later, we have made huge progress toward creating software that can understand and describe the content of visual data. But we've also discovered how far we must go before we can understand and replicate one of the fundamental functions of the human brain.

[Carnegie University]: "Computer vision has exploded over the past few years, and it is now able to identify objects with uncanny accuracy, leading to advances in everything from surveillance cameras to self-driving vehicles. There are two principal reasons for the rapid advances in computer vision, which uses AI to interpret and process the scenes viewed by cameras and other devices. 

  • First, because of the web, millions of images have now been labeled, allowing robotic vision systems to train themselves in how to identify what’s in a scene, using a form of artificial intelligence known as deep learning. 
  • Second, a new generation of graphics processing units, or GPUs, originally developed for the video gaming industry, has enabled much faster learning and identification of images. Also, the processing architecture used by deep networks mimics the human visual system, even to the point of apportioning the networks’ layers so they mirror the arrangement of functional brain areas humans use to see."



 [More to come ...]

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