Three year old children can make sense of what they see in photos and describe them to us, but even the most advanced computers have historically had difficulties with that same task. That’s quickly changing though, as computer scientists are developing powerful new ways to have computers identify what a photograph is showing.
The video above is a new TED talk given by Fei-Fei Li, a Stanford professor who’s one of the world’s leading experts on computer vision. She talks about her revolutionary ImageNet project that has changed how computers “see.” Read more…
The other day I created a Google+ album of photos from our holiday in France. Google’s AutoAwesome algorithms applied some nice Instagram-like filters to some of them, and sent me emails to let me have a look at the results. But there was one AutoAwesome that I found peculiar. It was this one, labeled with the word “Smile!” in the corner, surrounded by little sparkle symbols.
It’s a nice picture, a sweet moment with my wife, taken by my father-in-law, in a Normandy bistro. There’s only one problem with it. This moment never happened. Read more…
Data is embedded in our environment, in our behavior, and in our genes. Over the past two years, the world has generated 90% of all the data we have today. The information has always been there, but now we can extract and collect massive amounts of it.
Given the explosion of mobile photography, social media based photo sharing, and video streaming, it’s likely that a large portion of the data we collect and create comes in the form of digital images. Read more…
What if all advertising photos came with a number that revealed the degree to which they were Photoshopped? We might not be very far off, especially with recent advertising controversies and efforts to get “anti-Photoshop laws” passed. Researchers Hany Farid and Eric Kee at Dartmouth have developed a software tool that detects how much fashion and beauty photos have been altered compared to the original image, grading each photo on a scale of 1-5. The program may eventually be used as a tool for regulation: both publications and models could require that retouchers stay within a certain threshold when editing images.
Here’s the current state of imagery: still cameras can shoot HD video, video cameras can capture high quality stills, and data storage costs continue to fall. In the future, it might become commonplace for people to make photos by shooting uber-high quality video and then selecting the best still. However, as any photographer knows, selecting the best photograph from a series of photos captured in burst mode is already a challenge, so selecting a still from 30fps footage would be quite a daunting challenge.
To make the future easier for us humans, researchers at Adobe and the University of Washington are working on training computers to do the grunt work for us. One research project currently being done involves training a computer to automatically select candid portraits when given video of a person. The video above is a demo of the artificial intelligence in action.
Robots might not be able to convey emotions or tell stories through photographs, but one thing they’re theoretically better than humans at is calculating proportions in a scene, and that’s exactly what one robot at India’s IIT Hydrabad has been taught to do. Computer scientist Raghudeep Gadde programmed a humanoid robot with a head-mounted camera to perfectly obey the rule of thirds and the golden ratio. New Scientist writes,
The robot is also programmed to assess the quality of its photos by rating focus, lighting and colour. The researchers taught it what makes a great photo by analysing the top and bottom 10 per cent of 60,000 images from a website hosting a photography contest, as rated by humans.
Armed with this knowledge, the robot can take photos when told to, then determine their quality. If the image scores below a certain quality threshold, the robot automatically makes another attempt. It improves on the first shot by working out the photo’s deviation from the guidelines and making the appropriate correction to its camera’s orientation.
It’s definitely a step up from Lewis, a wedding photography robot built in the early 2000s that was taught to recognize faces.