There are countless practical applications of Image Recognition technology, but for every helpful use, there are plenty of “just because” utilizations of ComputerVision. One such example comes from Studio Diip, a Dutch company that has worked on projects ranging from vegetable recognition to automated card recognition, and which has used technology to allow fish in a tank to navigate a vehicle.
How does it work? In short, a camera positioned on the fish watches it swimming in its tank, analyzes this movement to determine the direction it is going, and then directs a car (mounted to the tank) to head in that direction. It’s not much of a scientific breakthrough, but it’s a fun idea.
How might this technology be applied in other ways? In what way can ComputerVision help improve your product?
ComputerVision has long been of interest to and utilized by the United States government and armed forces, but now it appears as though the army is using this technology to help transform soldiers into expert marksmen.
Tracking Point, a Texas-based startup that specializes in making precision-guided firearms, sold a number of “scope and trigger” kits for use on XM 2010 sniper rifles. The technology allows a shooter to pinpoint and “tag” a target, then use object-tracking technology, combined with a variety of variables (temperature, distance, etc.), to determine the most effective place to fire. The trigger is then locked until the person controlling the weapon has lined up the shot correctly, at which point he or she can pull the trigger.
To learn more about this technology and how it is implemented, watch the following video:
Since its launch in 2010, Pinterest has been the center of a variety of copyright issues, mostly pertaining to the unauthorized use of copyrighted material by users. The biggest problem in all of this is that most users are unknowningly violating copyright laws, which makes it harder to prosecute them. But recently, it seems as though Pinterest has found a fix for this quandary.
Rather than fighting one another, Pinterest has teamed up with (re: paid) Getty Images, a company that owns the rights to millions of images, many of which are repinned on Pinterest without proper credit. The agreement between the two dictates that image recognition software will now be used. This software will identify art and photos that belong to Getty Images and tag them with metadata. In this way, the artists will receive credit, Pinterest will avoid legal issues with Getty, and users will be protected as well. It’s a win-win-win.
Do you think this is a good fix? How else might image recognition software be used to give credit where credit is due?
Using an HD camera, a special lighting system, and a laser scanner, the setup can count grapes as small as 4mm in diameter, and using algorithms, is able to use the number of grapes and convert that to an estimated harvest yield. And while the margin of error is 9.8 percent, in humans, it’s 30, demonstrating that the Computer Vision system is more efficient and possibly more cost-effective.
In an effort to help protect and conserve endangered species, scientists have been tracking and tagging them for years. However, there are some species that are either too large in population or too sensitive to tagging, and researchers have been working on another way to track them.
Now, thanks to SLOOP, a new computer vision software program from MIT, identifying animals has never been easier. A human sorting through 10,000 images would likely take years to properly identify animals, but this computer program cuts down the manpower and does things much quicker. Through the use of pattern-recognition algorithms, the program is able to match up stripes and spots on an animal and return 20 images that are likely matches, giving researchers a much smaller and more accurate pool to work with. Then the researchers turn to crowdsourcing, and with the aid of adept pattern-matchers, are able to narrow things down even more, resulting in 97% accuracy. This will allow researchers to spend more practical time in the field working on conversation efforts instead of wasting time in front of a computer screen.
Computer Vision has many practical uses, ranging from security enhancement to making our lives easier, but what about art?
A new project, Shinseungback Kimyounghung, was launched by two South Koreans who are using Computer Vision to find faces in the clouds. This is similar to how children often lay on their backs and point out shapes in the sky, but instead, relies on computer algorithms to spot faces.
However, while the project appears artistic on surface level, examining it deeper reveals a study comparison how computers see versus how humans see. What the end result will be isn’t yet clear at this point, but it’s an interesting and thoughtful take on the subject nonetheless.
The robot, which is armed with a Kinect camera and relies on computer vision, has programming that includes a memory loaded with digital models and images of objects to aid in recognition. The goal is to create a robot that not only recognizes what it has already been taught, but grows that information on its own, without the database being expanded manually. It does this not only through simply seeing things, but also by exploring the environment and interacting with objects in it.
The Kinect camera helps to aid HERB in three-dimensional recognition, while the location of an object is also telling. Additionally, HERB can distinguish between items that move and those that don’t. As it interacts with its environment, it eventually is able to determine if something is an object, meaning if something can be lifted.
This information can later lead to robots that do things for humans, such as bringing them items or helping to clean. And while there is still a ways to go, the possibilities seem to be endless.
Instagram users threw a fit late last year when the popular photo app announced its new terms of services, many which users felt were a violation of privacy.
The main thing users took issue with was the ownership of photos, that is, if Instagram is allowed to take photos from its users and re-appropriate them as the company sees fit.
But what many people don’t realize is that their photos are already being used in the marketing and advertising worlds. Just consider gazeMetrix, a startup that uses computer vision and machine learning when sorting through photos on social media platforms, in order to recognize brand logos and trademarks being photographed.
In finding the use and appearance of these logos, companies are then able to promote their brands more effectively by targeting ads to the proper markets, see how these items are being used, and communicate to users of their specific products.
As we enter 2013, prognostications abound regarding object recognition technology and its likely impact on the economy, jobs and the human condition.
Some paint a grim picture of human obsolescence and slow growth. Others, a Utopian image of humans and machines extending each other’s capabilities that unlocks new economic vistas for the benefit of all.
In the less sanguine camp is Nobel Prize-winning economist, Paul Krugman who takes issue with the Congressional Budget Office’s (CBO) seemingly pat assumption that long term growth will occur at about the same rates we’ve seen for the past few decades.
On the more optimistic side is Bianca Bosker, Executive Tech Editor for the Huffington Post. She does a masterful job synthesizing a wide array of sources to make a balanced case.
Writing in the New York Times, Krugman points to Robert Gordon of Northwestern University and his contention that the age of growth that began in the late 1700s may be drawing to a close. He sees Gordon’s reasoning as a useful basis for doubting the CBO’s projections, however, Krugman does not agree with Gordon.
Gordon contends that growth has occurred unevenly owing to several discrete industrial revolutions that took us to the next level of major growth. The first was the steam engine. The second was the internal combustion engine, electrification and chemical engineering. The third is the information age and the Internet where smart machines are the payoff for fewer people than was the case in the second revolution.
Krugman posits that machines with ever-improving artificial intelligence and object recognition capabilities will likely fuel higher productivity and economic growth. He even states it would be “all too easy” to fear that smart machines will bring about the mass obsolescence of American workers.
If so, is Krugman saying the CBO’s long term projections are too conservative? Could this be a silver lining of sorts? He then asks the more unsettling question, “Who will benefit from this growth?”
Krugman promises in a future column to take up why the conventional wisdom underpinning long run budget projections is “all wrong.” And when he does, we should get a clearer view of his take on the roles object recognition, machine learning and human beings will play in the economy of tomorrow.
Bosker, in striking a balance between human obsolescence and human empowerment, seconds Kevin Kelly’s prediction in Wired that “robo surgeons” and “nannybots” will surely take over human jobs.
She then explores Google’s Project Glass as an example of wearable computers soon to arrive that observe and record our surroundings like an add-on brain.
Bosker quotes AI researcher Rod Furlan who speculates that Google Glass could soon help us find misplaced car keys. She predicts facial recognition will help us remember people’s names as soon as they come into view and bypass a potentially awkward encounter. And that object recognition could encourage us to skip an indulgent food we’d best not eat.
Attention shoppers with smartphone in hand: snap a photo of what someone else is wearing and find it on sale now!
L’Atelier of Paris reports on a startup from Sweden called OCULUSai whose Android app, Productify, lets you do just that (iOS version coming soon).
Productify recognizes the shoes, clothing or accessories of someone in your gaze and responds with a list of sites where to buy it. It also displays information on related products.
OCULUSai is focusing its efforts on the world of fashion and apparel and presented its solution at LeWeb in Paris in early December.
The technologies woven together include: computer vision, object recognition, image scanning, database marketing, and social media integration.
When you take a picture of an object, the app queries a database pre-populated with extensive fashion and apparel listings and related data about their visual qualities. In turn, the server, Oculus Brain, lists e-commerce sites where the item is for sale. You can share the product on Facebook and Twitter and recommend it to a friend.
I guess this means the days of people guessing how much you spent on that outfit are over!
So, how do you see augmented reality combining with e-commerce to transform your industry?