Image Recognition allows fish to navigate

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?

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ComputerVision steps up soldiers’ game

Photo by Bill Jamieson
Photo by Bill Jamieson

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:

Pinterest and Getty Images join forces with Image Recognition

 Image courtesy of Pinterest
Image courtesy of Pinterest

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?

 

Counting grapes with Computer Vision

Photo courtesy of Carnegie Mellon
Photo courtesy of Carnegie Mellon
It’s not secret that Computer Vision is an asset in the agricultural world, yet it’s still interesting to discover the new ways in which it is being put to you. For example, researchers at Carnegie Mellon University‚Äôs Robotics Institute published a study demonstrating how visual counting – one of the elementary Computer Vision concepts – is a way of estimating the yield of a crop of grapes.

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.

Computer Vision aids endangered species conservation efforts

Photo by Dr. Paddy Ryan/The National Heritage Collection
Photo by Dr. Paddy Ryan/The National Heritage Collection

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 sees faces in the clouds

Image courtesy of Shinseungback Kimyonghun
Image courtesy of Shinseungback Kimyonghun

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.

Robots discovering objects through Computer Vision

herbrobotWhile having a personal robot ala Judy in the television show, the Jetson’s, may have been the dream of every child, now such a thing is closer to becoming a reality.

Meet HERB, a Home-Exploring Robot Butler created by researchers at Carnegie Mellon University, as part of the Lifelong Robotic Object Discovery.

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.