Computer vision

MIT researchers have developed a trajectory-planning model that helps drones fly more safely at high speeds through previously unexplored areas, which could aid search-and-rescue missions through dense forests.

System prevents speedy drones from crashing in unfamiliar areas

Drones can fly at high speeds to a destination while keeping safe “backup” plans if things go awry.

October 25, 2019

MIT researchers are training a pair of generative adversarial networks, or GANs, to mimic the land, sea, and cloud textures seen in satellite images with the goal of eventually visualizing real-world sea-level rise. It’s one of many artificial intelligence research projects made possible by IBM and Google-donated cloud credits.

What a little more computing power can do

Commercial cloud service providers give artificial intelligence computing at MIT a boost.

September 16, 2019

MIT graduate students Logan Engstrom (left) and Andrew Ilyas have published five papers together exploring the vulnerabilities of computer vision systems, and they have half a dozen more in the pipeline.

Why did my classifier just mistake a turtle for a rifle?

Two longtime friends explore how computer vision systems go awry.

July 31, 2019

The GANpaint system developed at MIT can easily add features to an existing image. At left, the original photo of a kitchen; at right, the same kitchen with the addition of a window. Co-author Jun-Yan Zhu believes better understanding of GANs will help researchers be able to better stamp out fakery: “This understanding may potentially help us detect fake images more easily.”

Teaching artificial intelligence to create visuals with more common sense

An MIT/IBM system could help artists and designers make quick tweaks to visuals while also helping researchers identify “fake” images.

July 1, 2019

Yunzhu Li is a PhD student at the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL).

Teaching artificial intelligence to connect senses like vision and touch

MIT CSAIL system can learn to see by touching and feel by seeing, suggesting future where robots can more easily grasp and recognize objects.

June 17, 2019

Inkbit, a startup out of MIT, has created a multimaterial 3-D printer capable of printing extremely flexible materials more accurately than competitors, according to the founders.

A 3-D printer powered by machine vision and artificial intelligence

MIT startup Inkbit is overcoming traditional constraints to 3-D printing by giving its machines “eyes and brains.”

June 4, 2019

Artistic rendering of artificial intelligence and deep learning visual recognition systems

For better deep neural network vision, just add feedback (loops)

The DiCarlo lab finds that a recurrent architecture helps both artificial intelligence and our brains to better identify objects.

April 29, 2019

From an original transparent etching (far right), engineers produced a photograph in the dark (top left), then attempted to reconstruct the object using first a physics-based algorithm (top right), then a trained neural network (bottom left), before combining both the neural network with the physics-based algorithm to produce the clearest, most accurate reproduction (bottom right) of the original ...

Deep-learning technique reveals “invisible” objects in the dark

Method could illuminate features of biological tissues in low-exposure images.

December 12, 2018

MIT researchers describe an autonomous system for a fleet of drones to collaboratively search under dense forest canopies using only onboard computation and wireless communication — no GPS required.

Fleets of drones could aid searches for lost hikers

System allows drones to cooperatively explore terrain under thick forest canopies where GPS signals are unreliable.

November 1, 2018

MIT researchers have developed a “semantic parser” that learns through observation to more closely mimic a child’s language-acquisition process, which could greatly extend computing’s capabilities.

Machines that learn language more like kids do

Computer model could improve human-machine interaction, provide insight into how children learn language.

October 31, 2018

Recognizing the partially seen

Advances in computer vision inspired by human physiological and anatomical constraints are improving pattern completion in machines.

September 20, 2018

An MIT system uses an algorithm that can take 2-D videos and turn them into 3-D-printed “motion sculptures” that show how a human body moves through space.

Creating 3-D-printed “motion sculptures” from 2-D videos

CSAIL system could help athletes, dancers, and others better analyze how they move.

September 18, 2018

MIT computer scientists have developed a system that learns to identify objects within an image, based on a spoken description of the image.

Machine-learning system tackles speech and object recognition, all at once

Model learns to pick out objects within an image, using spoken descriptions.

September 18, 2018

MIT researchers have developed a module that helps artificial-intelligence systems fill in the gaps between video frames to improve activity recognition.

Helping computers fill in the gaps between video frames

Machine learning system efficiently recognizes activities by observing how objects change in only a few key frames.

September 13, 2018

PhD student Lucas Manuelli worked with lead author Pete Florence to develop a system that uses advanced computer vision to enable a Kuka robot to pick up virtually any object.

Robots can now pick up any object after inspecting it

Breakthrough CSAIL system suggests robots could one day be able to see well enough to be useful in people’s homes and offices.

September 10, 2018

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