A group of physicists and photographic experts from Washington University in St. Louis, Missouri have developed a photographic system that increases the number of photos per second that can be recorded in a moving object to 1011 pictures per second.
The computer versus the human mind will always be a question on the forefront of the news. The ultimate goals for these that push the computer brain further and faster are to replace a human at a specific task.
A group of researchers at Adobe, Microsoft, and MIT have taken normal objects and turned them into visual microphones. They discovered they can reproduce audio from silent video recordings by studying the movement of objects affected by sound waves traveling through the air.
The increase in accuracy in image recognition was seen at this year’s ImageNet Large Scale Visual Recognition Challenge (ILSVRC2014). The academic contest has been run annually since 2010, this year attracting thirty eight participants from thirteen different countries.
Never Ending Image Learner (NEIL) is a computer program that works 24/7 learning information about images that it finds on the internet. NEIL, which is housed at Carnegie Mellon University, is not looking for just any type of information. Rather, it’s goal is to learn common sense relationships found in everyday life.
Digital cameras have long been trying to replicate what is seen by the human eye. Now technology is advancing into what can be seen by a bug’s eye. These new devices are aimed to be capable of achieving a panoramic view with a sharp focus that can be seen at any distance.
The Goal-Line system can track the motion of the ball up to 500 images per second. The supporting computer vision software tracks the movement of all objects on the pitch in all the images and filters out the players, referees and all disturbing objects.
Chris Kluwe wants to look into the future of sports and think about how technology will help not just players and coaches, but fans. Here the former NFL punter envisions a future in which augmented reality will help people experience sports as if they are directly on the field — and maybe even help them see others in a new light, too.
The Facebook team adopted Deep Learning to apply to their face verification algorithm in lieu of well engineered features which is common in majority of contributions in this field.
For image processing and computer vision applications, the Wolfram Language provides built-in support for both programmatic and interactive modern industrial-strength image processing—fully integrated with the Wolfram Language's powerful mathematical and algorithmic capabilities.