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?
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?
Computer Vision is an interesting kind of technology in many ways, but perhaps one of the most notable things about it is how applicable it is and can be in our every day lives. And although it’s not necessarily a “new” field, it is something that is gaining popularity and recognition in the lives of “normal” people, meaning those who are not scientists, researchers, programmers, etc.
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.
Most everyone can recall a time when doctors or nurses have needed to draw blood or give shots and had trouble finding the proper veins. A company in California is all too familiar with this scenario, and in an effort to make the process of drawing blood more efficient, has created Veebot.
Veebot is essentially a robot phlebotomist. Relying on infrared lighting, an infrared camera, image analysis software, and ultrasound technology, the robot is able to locate the best vein for taking blood. All of this is checked and double-checked in the span of a few seconds in order to ensure that the first needle prick is successful.
Currently, the Veebot has been correct in its identification about 83% of the time, which is better than the average of humans. Once it has reached a 90% success rate, the company hopes to use the machine in clinical trials.
To see how this machine works, watch the video below:
Researchers at the Pittsburgh campus of Disney Research are using computer vision to analyze the patterns of field hockey players, in hopes of creating a new way for coachers and commentators to make sense of game data in real time. Furthermore, this technology can be used not only in field hockey, but any other kind of team sport with continuous play.
With a focus on player roles, the research zeroes in on the tactics, strategy, and style for players and their teams. Eight cameras recording high-definition video are used to record matches and data from these is analyzed against other matches. The compiled information can give insight into strengths and weaknesses of teams and solid strategies for how to better face their opponents.