Last month, researchers at the University of Central Florida presented a new facial recognition tool at the IEEE Computer Vision and Pattern Recognition conference in Columbus, Ohio.
While there is no shortage of facial recognition tools used by companies and governments the world over, this one is unique in that its aim is to unite or reunite children with their biological parents.
The university’s Center for Research in Computer Vision initially got to work by creating a database of more than 10,000 images of famous people–such as politicians and celebrities–and their children.
It works by using a specially designed algorithm that breaks the face down into sections, and using various facial parts as comparisons; they are then sorted according to which matches are the most likely.
Though software for this purpose already exists, this tool was anywhere from 3 to 10 percent better than those programs, and it naturally surpasses the recognition capabilities of humans, who base their decisions on appearance rather than the actual science of it. It also reaffirmed the fact that sons resemble their fathers more than their mothers, and daughters resemble their mothers more than their fathers.
Engineers at the University of California, San Diego, are using Computer Vision as a means of sorting cells, and thus far have been able to do so at a rate of 38 times faster than before. This process of counting and sorting cells is known as flow cytometry.
The analysis of the cells helps to categorize them based on their size, shape, and structure, and also can distinguish if they are benign or malignant, information that could be useful for clinical studies and stem cell characterization.
While this type of research was occurring before, it’s a job that has traditionally taken a lot of time. But now, the use of a camera on a microscope can analyze information faster–cutting the time from between 0.4 and 10 seconds to observe and analyze a single frame down to between 11.94 and 151.7 milliseconds.
In what ways do you see this technology making advancements in the medical and clinical world? How else can you imagine it benefitting science?
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.
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.
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.