Illegal workers seem to be a given in various industries around the world, but now Australia has begun cracking own on those looking to exploit the system. How? With computer vision, of course.
Immigration officials in the land down under are using facial recognition software to identify people who have either created new identities or stolen those of legitimate persons in order to obtain employment.
In a recent raid on farm workers, six illegal persons were detained for over-staying their visas and working. This kind of capture not only causes problems for the illegal person, but also for the employers, who can be charges up to $66,000 for each illegal worker. Specific detail about what kind of programs are being used was not provided, but could entail collaborating with state and federal agency databases in Australia and abroad.
Add cancer to the list of medical problems computer vision can be used in diagnosing and treating.
At the Lawrence Berkeley National Laboratory in California, researchers have created a program that analyzes images of tumors (of which there are thousands, stored in the database of The Cancer Genome Atlas project). This program relies on an algorithm that sorts through image sets and helps identify tumor subtypes – a process which is not so easy considering no two tumors are alike.
After sorting through the images, it categorizes them according to subtype and composition of organizational structure, and then matches those things up with clinical data that give an idea of how a patient affected with a certain tumor will react to treatment.
For more on what this program can do and how it can be used, see the press release here.
Computer Vision can seem like a daunting field to those not familiar with it. However, the University of Canterbury in New Zealand has created an online, interactive “textbook” geared at teaching high school students more about this creative, emerging field.
Just watch this video to see how easy explaining Computer Vision can be and how applicable it is in our everyday lives.
With a new product designed by Delphi, this exact technology could be coming to your own car.
According to the company, the technology in question “uses a light source and camera to project a line pattern onto the subject’s face. The camera then ‘sees’ and records the subject’s two-dimensional (2D) facial fingerprint, comparing that image to a database of stored 2D facial fingerprints for a possible match. A ‘positive’ match with the proper stored image means the person is recognized. Recognition then triggers an action, for example, approval of a credit sale or unlocking of a door.”
Other possible uses for this are that the vehicle remembers settings, such as where the seat is positioned, what station the driver listens to, and how warm or cold the temperature should be.
However, although the technology was initially created for vehicles, it has other practical applications. Theoretically, any kind of security system requiring visual identification could benefit from this technology. What ways do you envision it being used?
These clocks not only eliminate the old-fashioned standard of “punching in” and “punching out,” but are also a way to ensure that the proper employee is clocking in, and that workplace fraud isn’t occurring.
Adding to its line of forward-thinking time clock systems, Wirelesstimeclock unveiled another new clock last month. The company’s Facial Recognition Clock touts itself as the most inexpensive of its breed in the U.S., and allows employees to punch in and out either through facial recognition, RFID badge, or pin. Not only that, but employees have access to the system via smartphone, where they can view the log and ensure that the hours they worked were properly recorded.
According to the recently published findings, worms are one of many tiny multi-cellular living beings that act as effective test subjects for researching genetics.
Using artificial intelligence–combined with advanced image processing–scientists are able to inspect and process these worms–known as Caenorhabditis elegans–more quickly and efficiently than in the past. With a camera that records 3D images of worms and compares them against a model of abnormal worms, the machine can not only tell the difference, but learns from it, teaching itself as it goes.
Picking out distinct factors better than humans can on their own, this technology highlights genetic mutations between the worms, which can be a key for unlocking further advances in genetic research and testing in humans in years to come.
These sharks boast a unique pattern of white spots on dark skin, which is similar to the kind of “blob extraction” that astrophysicists use to identify stars and other bodies in space.
Once it paved the way, this technology then opened the doors for other types of identification–this time, for dolphins. Through the use of manual photo identification, dolphins were able to be identified based on the marks on their dorsal fins. Yet even this process was too time consuming.
Recently, however, a computer science professor at Eckerd College has, along with the help of her students, created the program DARWIN (Digital Analysis and Recognition of Whale Images on a Network). This speeds up the process by using a combination of computer vision and signal processing techniques to make the process automated, as opposed to manual.
After creating an outline of the fin of a bottlenose dolphin, the system builds up a database and, using computer-vision algorithms, matches up identified fins in the database with those that are unknown. The images then are displayed in a ranking system, showing both matches that are highly probable, as well as those that aren’t as likely.
This is an interesting development for sea animals, because the identification process is faster and more reliable. But what practical applications might be involved? What benefits will researchers of marine life have from programs like this? And what applications can this have in other realms of computer vision and identification?
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.
In fact, those concerned about violation of privacy associated with facial recognition databases, eye scans, or fingerprint matching, might find ear biometrics to be much more appealing as a way of identifying and matching.
A project conducted by RMIT‘s school of media and communication has come up with findings that the brains of honey bees are capable of tackling complex visual problems, as well as creating and applying rules to adapt to those specific scenarios.
An example of this in humans is the ability to encounter a situation, such as coming up to an intersection, and acting accordingly. This involves a range of realizations and responses, such as observing the traffic light, gauging the speed of vehicles and looking out for pedestrians or bicyclists that might also obstruct the flow of traffic. Based on the information being fed to our brains, we are able to make split-second decisions, something which computers aren’t yet fully capable of doing. This is because it involves processing more than one kind of complex task, and these tasks don’t appear to have anything in common, in the “mind” of a computer.
However, that’s not to say that computers can’t eventually learn this skill, too. By studying the brains of honey bees, researchers hope to learn how this works in them, and then apply those same things to computers, allowing them to process visual inputs efficiently and effectively.