Grammar-like algorithm identifies actions in video

Photo courtesy of
Photo courtesy of








VISAPP Computer Vision conference extends submission deadline



Computer Vision studies bird flocking behavior

Photo courtesy of Andreas Trepte.
Photo courtesy of Andreas Trepte.
Flocking is a behavior exhibited in birds, which is similar to how land animals join together in herds. And while there is an intricate pattern to this flocking, it’s difficult to establish exactly how birds communicate to keep this form. Their movements are synchronous, but the question is: how do birds on the outer edges of the flock stay in sync and help guide the group? Luckily, we have computer vision to help answer that question.

Before, scientists used to simulate this behavior and then compare it to what occurs with birds in real life in an attempt to demonstrate the how and the why. However, now computer vision can measure both position and velocity of objects in a frame, thanks to the work of William Bialek at Princeton University, which is demonstrating that birds are capable of matching the speed and direction of their neighbor birds.

Additionally, the concept of “critical point” helps explain this, showing that the social desires of the birds overwhelms the motivation of each individual bird, as they work toward flying as a collective flock and not as solo birds.

There still remains more to be seen and explored, but check out this study for further reading.

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



Automated baked-goods identification can benefit businesses

Researchers at the University of Hyogo, alongside Brain Corporation, have created a computer-vision system that works to develop individual baked goods in a second.

The system had its first test-run at a bakery in Tokyo, where employers are benefitting. This is because their new employees who haven’t yet learned the ropes, or part-timers who don’t know the name of every kind of baked good, can still work the cash registers. Additionally, when there are long lines, it can speed up the check-out process, making the entire operation run more efficiently and smoothly.

While the system works relatively well, there still are some kinks to work out. For example, baked goods are easily distinguish by their shapes and toppings, but when it comes to sandwiches, the machine has a tougher time telling them apart.

Luckily, there are other companies out there with the technology to build even better versions of this same sample system. For example, the people at ImageGraphicsVideo can build a similar system which also has a learning capability. This means that whoever is using the system can input, or “teach,” new items to the computer. Not only that, but the user can point out when items are incorrectly identified, which the program then learns and uses in the future to avoid making the same mistakes.

Guaranteed win at Rock, Paper, Scissors with computer vision

Rock, Paper, Scissors is a famous hand game known throughout the world and dating back to the Han Dynasty. Largely a game of luck, it can be won by the most skilled of players based on various strategies such as studying the patterns of an opponent and playing against their predictability.

However, there is now a machine that is virtually impossible to beat. Researchers in the Ishikawa Oku Laboratory at the University of Tokyo have created a robot that has a 100% success rate at winning matches of Rock, Paper, Scissors.

Using computer vision, the robot is able to recognize the hand movements of its human opponent and respond with a winning match. This “cheat” occurs in the period of 1 millisecond, an amount of time that the human mind cannot see, process and respond to, ensuring the computer always wins.

How might this kind of technology be applied in more practical realms?