New algorithms power a prototype system for helping visually impaired users avoid obstacles and identify objects. Courtesy of the researchers.

Computer scientists have been working for decades on automatic navigation systems to aid the visually impaired, but it’s been difficult to come up with anything as reliable and easy to use as the white cane, the type of metal-tipped cane that visually impaired people frequently use to identify clear walking paths.

White canes have a few drawbacks, however. One is that the obstacles they come in contact with are sometimes other people. Another is that they can’t identify certain types of objects, such as tables or chairs, or determine whether a chair is already occupied.

Researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) have developed a new system that uses a 3-D camera, a belt with separately controllable vibrational motors distributed around it, and an electronically reconfigurable Braille interface to give visually impaired users more information about their environments.

The system could be used in conjunction with or as an alternative to a cane. In a paper they’re presenting this week at the International Conference on Robotics and Automation, the researchers describe the system and a series of usability studies they conducted with visually impaired volunteers.

“We did a couple of different tests with blind users,” says Robert Katzschmann, a graduate student in mechanical engineering at MIT and one of the paper’s two first authors. “Having something that didn’t infringe on their other senses was important. So we didn’t want to have audio; we didn’t want to have something around the head, vibrations on the neck — all of those things, we tried them out, but none of them were accepted. We found that the one area of the body that is the least used for other senses is around your abdomen.”

Katzschmann is joined on the paper by his advisor Daniela Rus, an Andrew and Erna Viterbi Professor of Electrical Engineering and Computer Science; his fellow first author Hsueh-Cheng Wang, who was a postdoc at MIT when the work was done and is now an assistant professor of electrical and computer engineering at National Chiao Tung University in Taiwan; Santani Teng, a postdoc in CSAIL; Brandon Araki, a graduate student in mechanical engineering; and Laura Giarré, a professor of electrical engineering at the University of Modena and Reggio Emilia in Italy.

Parsing the world

The researchers’ system consists of a 3-D camera worn in a pouch hung around the neck; a processing unit that runs the team’s proprietary algorithms; the sensor belt, which has five vibrating motors evenly spaced around its forward half; and the reconfigurable Braille interface, which is worn at the user’s side.

The key to the system is an algorithm for quickly identifying surfaces and their orientations from the 3-D-camera data. The researchers experimented with three different types of 3-D cameras, which used three different techniques to gauge depth but all produced relatively low-resolution images — 640 pixels by 480 pixels — with both color and depth measurements for each pixel.

The algorithm first groups the pixels into clusters of three. Because the pixels have associated location data, each cluster determines a plane. If the orientations of the planes defined by five nearby clusters are within 10 degrees of each other, the system concludes that it has found a surface. It doesn’t need to determine the extent of the surface or what type of object it’s the surface of; it simply registers an obstacle at that location and begins to buzz the associated motor if the wearer gets within 2 meters of it.

Chair identification is similar but a little more stringent. The system needs to complete three distinct surface identifications, in the same general area, rather than just one; this ensures that the chair is unoccupied. The surfaces need to be roughly parallel to the ground, and they have to fall within a prescribed range of heights.

Tactile data

The belt motors can vary the frequency, intensity, and duration of their vibrations, as well as the intervals between them, to send different types of tactile signals to the user. For instance, an increase in frequency and intensity generally indicates that the wearer is approaching an obstacle in the direction indicated by that particular motor. But when the system is in chair-finding mode, for example, a double pulse indicates the direction in which a chair with a vacant seat can be found.

The Braille interface consists of two rows of five reconfigurable Braille pads. Symbols displayed on the pads describe the objects in the user’s environment — for instance, a “t” for table or a “c” for chair. The symbol’s position in the row indicates the direction in which it can be found; the column it appears in indicates its distance. A user adept at Braille should find that the signals from the Braille interface and the belt-mounted motors coincide.

In tests, the chair-finding system reduced subjects’ contacts with objects other than the chairs they sought by 80 percent, and the navigation system reduced the number of cane collisions with people loitering around a hallway by 86 percent.

In 2016, MIT CSAIL researchers worked on a range of projects in robotics, theory, wireless technology, software systems, and other disciplines. Image: CSAIL

Machines that predict the future, robots that patch wounds and wireless emotion-detectors are just a few of the exciting projects that came out of MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) this year. Here’s a sampling of 16 highlights from 2016 that span the many computer science disciplines that make up CSAIL.

Robots for exploring Mars — and your stomach

• A team led by CSAIL director Daniela Rus developed an ingestible origami robot that unfolds in the stomach to patch wounds and remove swallowed batteries.
• Researchers are working on NASA’s humanoid robot, “Valkyrie,” who will be programmed for trips into outer space and to autonomously perform tasks.
• A 3-D printed robot was made of both solids and liquids and printed in one single step, with no assembly required.

Keeping data safe and secure

• CSAIL hosted a cyber summit that convened members of academia, industry, and government, including featured speakers Admiral Michael Rogers, director of the National Security Agency; and Andrew McCabe, deputy director of the Federal Bureau of Investigation.
• Researchers came up with a system for staying anonymous online that uses less bandwidth to transfer large files between anonymous users.
• A deep-learning system called AI2 was shown to be able to predict 85 percent of cyberattacks with the help of some human input.

Advancements in computer vision

• A new imaging technique called Interactive Dynamic Video lets you reach in and “touch” objects in videos using a normal camera.
• Researchers from CSAIL and Israel’s Weizmann Institute of Science produced a movie display called Cinema 3D that uses special lenses and mirrors to allow viewers to watch 3-D movies in a theater without having to wear those clunky 3-D glasses.
• A new deep-learning algorithm can predict human interactions more accurately than ever before, by training itself on footage from TV shows like “Desperate Housewives” and “The Office.”
• A group from MIT and Harvard University developed an algorithm that may help astronomers produce the first image of a black hole, stitching together telescope data to essentially turn the planet into one large telescope dish.

Tech to help with health

• A team produced a robot that can help schedule and assign tasks by learning from humans, in fields like medicine and the military.
• Researchers came up with an algorithm for identifying organs in fetal MRI scans to extensively evaluate prenatal health.
• A wireless device called EQ-Radio can tell if you’re excited, happy, angry, or sad, by measuring breathing and heart rhythms.

Algorithms, systems and networks

• A system called “Polaris” was found to load web pages 34 percent faster by decreasing network trips.
• A team analyzed ant-colony behavior to create better algorithms for network communication, for applications such as social networks and collective decision-making among robot swarms.
• Researchers trained neural networks to explain themselves by providing rationales for their decisions.

MIT researchers have developed a new approach to training speech-recognition systems that doesn’t depend on transcription. Instead, their system analyzes correspondences between images and spoken descriptions of those images, as captured in a large collection of audio recordings. Image: MIT News

Speech recognition systems, such as those that convert speech to text on cellphones, are generally the result of machine learning. A computer pores through thousands or even millions of audio files and their transcriptions, and learns which acoustic features correspond to which typed words.

But transcribing recordings is costly, time-consuming work, which has limited speech recognition to a small subset of languages spoken in wealthy nations.

At the Neural Information Processing Systems conference this week, researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) are presenting a new approach to training speech-recognition systems that doesn’t depend on transcription. Instead, their system analyzes correspondences between images and spoken descriptions of those images, as captured in a large collection of audio recordings. The system then learns which acoustic features of the recordings correlate with which image characteristics.

“The goal of this work is to try to get the machine to learn language more like the way humans do,” says Jim Glass, a senior research scientist at CSAIL and a co-author on the paper describing the new system. “The current methods that people use to train up speech recognizers are very supervised. You get an utterance, and you’re told what’s said. And you do this for a large body of data.

“Big advances have been made — Siri, Google — but it’s expensive to get those annotations, and people have thus focused on, really, the major languages of the world. There are 7,000 languages, and I think less than 2 percent have ASR [automatic speech recognition] capability, and probably nothing is going to be done to address the others. So if you’re trying to think about how technology can be beneficial for society at large, it’s interesting to think about what we need to do to change the current situation. And the approach we’ve been taking through the years is looking at what we can learn with less supervision.”

Joining Glass on the paper are first author David Harwath, a graduate student in electrical engineering and computer science (EECS) at MIT; and Antonio Torralba, an EECS professor.

Visual semantics

The version of the system reported in the new paper doesn’t correlate recorded speech with written text; instead, it correlates speech with groups of thematically related images. But that correlation could serve as the basis for others.

If, for instance, an utterance is associated with a particular class of images, and the images have text terms associated with them, it should be possible to find a likely transcription of the utterance, all without human intervention. Similarly, a class of images with associated text terms in different languages could provide a way to do automatic translation.

Conversely, text terms associated with similar clusters of images, such as, say, “storm” and “clouds,”  could be inferred to have related meanings. Because the system in some sense learns words’ meanings — the images associated with them — and not just their sounds, it has a wider range of potential applications than a standard speech recognition system.

To test their system, the researchers used a database of 1,000 images, each of which had a recording of a free-form verbal description associated with it. They would feed their system one of the recordings and ask it to retrieve the 10 images that best matched it. That set of 10 images would contain the correct one 31 percent of the time.

“I always emphasize that we’re just taking baby steps here and have a long way to go,” Glass says. “But it’s an encouraging start.”

The researchers trained their system on images from a huge database built by Torralba; Aude Oliva, a principal research scientist at CSAIL; and their students. Through Amazon’s Mechanical Turk crowdsourcing site, they hired people to describe the images verbally, using whatever phrasing came to mind, for about 10 to 20 seconds.

For an initial demonstration of the researchers’ approach, that kind of tailored data was necessary to ensure good results. But the ultimate aim is to train the system using digital video, with minimal human involvement. “I think this will extrapolate naturally to video,” Glass says.

Merging modalities

To build their system, the researchers used neural networks, machine-learning systems that approximately mimic the structure of the brain. Neural networks are composed of processing nodes that, like individual neurons, are capable of only very simple computations but are connected to each other in dense networks. Data is fed to a network’s input nodes, which modify it and feed it to other nodes, which modify it and feed it to still other nodes, and so on. When a neural network is being trained, it constantly modifies the operations executed by its nodes in order to improve its performance on a specified task.

The researchers’ network is, in effect, two separate networks: one that takes images as input and one that takes spectrograms, which represent audio signals as changes of amplitude, over time, in their component frequencies. The output of the top layer of each network is a 1,024-dimensional vector — a sequence of 1,024 numbers.

The final node in the network takes the dot product of the two vectors. That is, it multiplies the corresponding terms in the vectors together and adds them all up to produce a single number. During training, the networks had to try to maximize the dot product when the audio signal corresponded to an image and minimize it when it didn’t.

For every spectrogram that the researchers’ system analyzes, it can identify the points at which the dot-product peaks. In experiments, those peaks reliably picked out words that provided accurate image labels — “baseball,” for instance, in a photo of a baseball pitcher in action, or “grassy” and “field” for an image of a grassy field.

In ongoing work, the researchers have refined the system so that it can pick out spectrograms of individual words and identify just those regions of an image that correspond to them.

“Possibly, a baby learns to speak from its perception of the environment, a large part of which may be visual,” says Lin-shan Lee, a professor of electrical engineering and computer science at National Taiwan University. “Today, machines have started to mimic such a learning process. This work is one of the earliest efforts in this direction, and I was really impressed when I first learned of it.”

“Perhaps even more exciting is just the question of how much we can learn with deep neural networks,” adds Karen Livescu, an assistant professor at the Toyota Technological Institute at the University of Chicago. “The more the research community does with them, the more we realize that they can learn a lot from big piles of data. But it is hard to label big piles of data, so it’s really exciting that in this work, Harwath et al. are able to learn from unlabeled data. I am really curious to see how far they can take that.”

Read the paper here.

Robot from Computer Science and Artificial Intelligence Lab suggests where to move patients and who should do C-sections.

By: Adam Conner-Simons

Today’s robots are awkward co-workers because they are often unable to predict what humans need. In hospitals, robots are employed to perform simple tasks like delivering supplies and medications but they have to be explicitly told what to do.

A team from MIT’s Computer Science and Artificial Intelligence Lab (CSAIL) thinks that this will soon change, and that robots might be most effective by helping humans perform one of the most complex tasks of all: scheduling.

In a pair of new papers, CSAIL researchers demonstrate a robot that, by learning from human workers, can help assign and schedule tasks in fields ranging from medicine to the military.

In one paper, the team demonstrated a robot that assisted nurses in a labor ward, where it made recommendations on everything from where to move a patient to which nurse to assign to a C-section.

“The aim of the work was to develop artificial intelligence that can learn from people about how the labor and delivery unit works, so that robots can better anticipate how to be helpful or when to stay out of the way – and maybe even help by collaborating in making challenging decisions,” says MIT professor Julie Shah, the senior author on both papers.

In a second paper, the same system was put to the test in a videogame that simulates missile-defense scenarios. In the game, which was developed by Lincoln Laboratory researchers and involves using decoy missiles to ward off enemy attacks, the system even occasionally outperformed human experts at reducing both the number of missile attacks and the overall cost of decoys.

The labor-ward paper was presented at the recent Robotics: Science and Systems (RSS) Conference and was co-written by PhD student Matthew Gombolay, CSAIL postdocs Xi Jessie Yang and Brad Hayes, and Dr. Neel Shah and Toni Golen from Beth Israel Deaconess Medical Center, which is where the study took place.

The Navy-simulation paper is being presented at this week’s International Joint Conference on Artificial Intelligence (IJCAI), and was co-written by Gombolay and the  Lincoln Lab’s Reed Jensen, Jessica Stigile and Sung-Hyun Son.

Right on schedule

From visiting hospitals and factories, Shah and Gombolay found that a subset of workers are extremely strong schedulers, but can’t easily transfer that knowledge to colleagues.

“Figuring out what makes certain people good at this often seems like a mystery,” Gombolay says. “Being able to automate the task of learning from experts – and to then generalize it across industries – could help make many businesses run more efficiently.”

A particularly tough place for scheduling are hospitals. Labor wards’ head nurses have to try to predict when a woman will arrive in labor, how long labor will take, and which patients will become sick enough to require C-sections or other procedures.

They are deluged with an endless stream of challenging split-second decisions that include assigning nurses to patients, patients to beds, and technicians to surgeries. At Beth Israel, the head nurse has to coordinate 10 nurses, 20 patients and 20 rooms at the same time, meaning that the number of distinct scheduling possibilities adds up to a staggering 2 to the one millionth power, which is more than the number of atoms in the universe.

“We thought a complex environment like a labor ward would be a good place to try to automate scheduling and take this significant burden off of workers,” says Gombolay.

How it works

Like many AI systems, the team’s robot was trained via “learning from demonstration,” which involves observing humans’ performances of tasks. But Gombolay says that researchers have never been able to apply this technique to scheduling, because of the complexity of coordinating multiple actions that can be very dependent on each other.

To overcome this, the team trained its system to look at several actions that human schedulers make, and compare them to all the possible actions that are not made at each of those moments in time. From there, it developed a scheduling policy that can respond dynamically to new situations that it has not seen before.

“Rather than considering actions in isolation of each other, we crafted a model that understands why one action is better than the alternatives,” says Shah. “By considering all such comparisons, you can learn to recommend which action will be most helpful.”

The policy is “model-free,” meaning that the nurses do not have to train the robot by painstakingly ranking each possible action in each possible scenario by hand.

“You can put the robot on the labor floor, and, just by watching humans doing the different tasks, it will understand how to coordinate an efficient schedule,” says Gombolay.

The results

With this framework, the system – which the team has dubbed “apprenticeship scheduling” – can anticipate room assignments and suggest which nurses to assign to patients for C-sections and other procedures.

The approach was evaluated through experiments in which a robot provided decision-support to nurses and doctors as they made decisions on a labor floor.

Using the system on a Nao robot, nurses accepted the robot’s recommendations 90 percent of the time. The team also demonstrated that human subjects weren’t just blindly accepting advice – the robot delivered consciously bad feedback that was rejected at the same rate of 90 percent, showing that the system was trained to distinguish between good and bad recommendations.

Nurses had almost uniformly positive feedback about the robot. One said that it would “allow for a more even dispersion of workload,” while another said that it would be particularly helpful for “new nurses [who] may not understand the constraints and complexities of the role.”

“A great potential of this technology is that good solutions can be spread more quickly to many hospitals and workplaces,” says Dana Kulic, an associate professor of computer engineering at the University of Waterloo. “ For example, innovative improvements can be distributed rapidly from research hospitals to regional health centres.”

Shah says that the new techniques have many uses, from turning robots into better collaborators to helping train new nurses, but the goal is not to develop robots that fully make decisions on their own.

“These initial results show there is tremendous potential for machines to collaborate with us in rich ways that will enhance many sectors of the economy,” says Shah. “The awkward robots of the past will be replaced by valued team members.”

The RSS paper was supported by the National Science Foundation, CRICO Harvard Risk Management Foundation, and Aldebaran Robotics Inc. The IJCAI paper was supported the National Science Foundation and the U.S. Navy.

Read the paper here. 

Via Wikimedia Commons

In episode twenty one  we talk with Quaid Morris of the University of Toronto, who is using machine learning to find a better way to treat cancers. Ryan introduces us to expectation maximization and we take a listener question about how to master machine learning.

I like Star Wars. I like technology. I like philosophy. I like teaching. I like the occasional meme. I like robots. I like a lot of things.

But if I were to give an accurate account of the things that define me, that truly make up my identity, I would focus on my liking philosophy, the way it informs my thoughts about technology and the world. I would probably describe some of the research I do. There’s a good chance I’d talk about one of the courses I teach. If the conversation went long enough, I just might mention something about Star Wars.

My liking philosophy and technology translates into my spending a great deal of my daily waking hours thinking, reading, and writing about philosophy and technology. My liking Star Wars translates into my buying a LEGO kit once in a while and building it with my daughters, or spending a couple of hours now and again watching a movie I’ve seen a few dozen times.

Philosophy and technology are a big part of my identity. Star Wars is not.

But to our Facebook friends, lets call them our ffriends for short, none of us can honestly know how we appear.

Facebook uses automated filtering algorithms, little snippets of software code, to decide which of your posts will be made visible on your ffriends’ news feeds.

Facebook uses automated filtering algorithms, little snippets of software code, to decide which of your posts will be made visible on your ffriends’ news feeds. The algorithms work in the background, like little robots, constantly monitoring user activity and making automated decisions about each post on the system. But how those little automated bots select and filter your posts is not clear: Facebook doesn’t publish the details of the algorithms. Yet, the details can make a big splash.

Many of you will have read about the public outrage surrounding Facebook’s latest ethics scandal (if you haven’t, here is a fantastic summary in The Atlantic). It involves a study Facebook conducted (perhaps using you as a participant) to see if they could establish a link between the “emotional content” contained in a user’s news feed, and that user’s level of activity on Facebook. In short, they wanted to see if being exposed to more happy, upbeat news items would cause you to spend more time posting on Facebook than sad ones.

To test their hypothesis, they tinkered with their automated filtering algorithms during one week in 2012. The filters were set to bias your news feed: depending on the study category you were in you might have seen more “emotionally positive” posts than usual, or more “emotionally negative” posts. Their findings (sort of) confirmed a link between the emotional content in a user’s news feed and a user’s posting habits. Happy posts in a news feed appear to make you post more to Facebook.

Facebook recently published their findings. Users are outraged. But why?

Much of the commentary on the outrage has focused on the fact that Facebook appears to be attempting to actively manipulate its users by engaging them emotionally, and on the fact that Facebook conducted its research unethically. Danah Boyd has written about the reaction.

But, hold on. That shouldn’t surprise us, should it? On the one hand, Facebook, like so many other organizations whose mission is to manipulate you into doing this or that, purchasing this or that, or thinking this or that, is just another one of those kinds of organizations. And, as Danah Boyd points out, the research ethics issues at play are being a bit overstated. There may be some problems with the way the ethics of this study were handled, but they are minor and not at all unique to Facebook or this study.

There is another, more problematic reason to feel bothered, even outraged, at Facebook’s current design practices.

For many of us Facebook has become the de facto tool used to communicate with friends (and ffriends). Indeed, like credit cards, it has become increasingly difficult to avoid using Facebook. Oh sure, you can opt-out. But that puts you at a certain social disadvantage. You’ll miss out on conversations, events, and other goings on. Because it is such a central actor in so many of our communications, Facebook is now the thing many of us use to sculpt our image. Put another way, Facebook has become the primary conversational and interactional medium through which a vast number of us express and shape our identities.

Because so many people, rightly or wrongly, use the platform as their primary communication medium, Facebook’s news item filtering algorithms can have a direct impact on what kind of person your ffriends think you are. How does this happen? Let’s say you post 10 news items in a day, eight of them having to do with serious topics like NSA spying or the current uprising in the Ukraine, and two of them containing memes. If the algorithm is designed to systematically “demote” your posts about world affairs and “promote” your meme posts, over time you start to look more like a person obsessed with memes and less like a person interested in world affairs. In my case, it’s entirely possible many of my ffriends think I spend most of my day thinking about Star Wars.

That’s a problem.

The net result is that Facebook’s algorithms, those little bots made up of so many lines of code, actively bend your identity based on conditions completely invisible to you, by making decisions on your behalf about which of your posts to place on your ffriends’ news feeds.

[ASIDE: At this point I have to apologize to all of my ffriends that I may have unfairly “silenced” in my news feed over the years. It is entirely possible that you are the victim of Facebook’s identity bending algorithms, and that you are not actually obsessed with LOLZ cats, socially awkward penguins, or motivational jpgs meant to inspire me to be a better person. Though you might have posted numerous interesting and thought-provoking comments to your feed, it might be the case that I was simply not allowed to see you for who you really are. Then again, maybe I was. We may never know.]

I have written about the ethics of automation, and some of the issues that can arise when automation technology is designed to make decisions on behalf of users. When decisions are designed into technologies that provide material answers to moral questions, those technologies can subject users to a form of paternalism that is ethically problematic. In this case, Facebook’s algorithms are making decisions about what kind of person you appear to be to your ffriends. Your identity is a deeply personal moral issue.

Promoting your posts based on invisible criteria is ethically different than promoting your posts based on transparent criteria. The two are not ethically on a par with one another. When filtering criteria are made transparent you can make an informed decision about using the platform. For example, if Facebook explains to users that the system is designed to promote posts that are most popular among your ffriends, you will have a relatively accurate idea of how “visible” your posts are to your ffriends and why their posts are showing up in your feed.

Furthermore, when you have no idea how you are being made to appear to your ffriends, you are hardly the author of your identity. If Facebook continues to apply filtering algorithms based on invisible criteria, you will never be more than the co-author of your identity.

This, I think, helps to explain our negative gut reaction to Facebook’s “emotional manipulation” study much more than any actual claim to emotional manipulation can. After all, there are just so many similar examples of emotional manipulation out there that we tacitly accept. That Facebook is willing to make decisions about your outward appearance, however, that they are willing to manipulate your identity without telling you how, gives you good reason to distrust Facebook and its identity bending algorithms.

Robohub is an online platform that brings together leading communicators in robotics research, start-ups, business, and education from around the world. Learn more about us here. If you liked this article, you may also be interested in:

• Is surveillance the new business model for consumer robotics?
• DARPA’s Gill Pratt on Google’s robotics investments
• The robots behind your Internet shopping addiction: How your wallet is driving retail automation
• Big deals: What it means to have the giants investing in robotics
• An ethical dilemma: When robot cars must kill, who should pick the victim?
• Robots Podcast: A Code of Ethics for HRI Practitioners
• Kids with wheels: Should the unlicensed be allowed to ‘drive’ autonomous cars?
• What should a robot do? Designing robots that know right from wrong

See all the latest robotics news on Robohub, or sign up for our weekly newsletter.

Researchers at Wakayama University are developing a system that manipulates the color, tone and contrast of printed materials, and could be used as an aid for users with vision impairments such as low vision or color blindness.

“In this research, we use a projector and camera to freely manipulate the tone and contrast of printed materials like this, by shining light onto them.”

“Right now, white light from the projector is hitting the material. In this device, there’s a camera, which photographs the scene. By controlling the projected light based on the scene, we can make the colors more vivid, for example, like this. The way the system works is, to put it very simply: Blue light is projected onto blue parts, and red light onto red parts, making the colors more vivid.”

“By changing the image processing, we can change color tones as well, like this. We can also make the picture monochrome, make the brightness uniform, or increase the contrast.”

“The point about this technology is, this kind of processing can be done dynamically. In other words, even if the printed material is moved, the system can track it and emphasize the colors.”

“When the light is projected, the actual color tone changes. So, the camera captures the altered color tone. The problem with that is, you can no longer tell how many real colors there are, or how many colors should be projected next. In this research, the system calculates the physical reflectivity of the captured image and the projected image. That estimation is a major feature of this technology.”

“One possible application for this is augmenting human vision. For example, if a person is colorblind, this system could adjust color tones so colors can be distinguished, even if there are combinations of confusing colors. Or a person with cataracts could be helped to see other people’s expressions, by emphasizing outlines.”

Also, by arranging the camera and projector into the same position optically, this image processing technique can be applied to 3D objects as well.

“We’ve added a texture adjustment algorithm, to make opaque objects look semi-transparent, or emphasize their luster, to make them look metallic.”

“We’re currently looking for businesses that might have applications for this technology.”