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Researchers utilize shadows to create 3D scene models, incorporating objects that are normally obstructed from sight.

Artificial Intelligence, Autonomous vehicles, Computer science and technology, Computer vision, Imaging, Light, Machine learning, Media Lab, Research, School of Architecture and Planning, Sensors, UncategorizedJune 18, 202440Views0Likes0Comments
Researchers from MIT and Meta have developed a computational vision technique, named PlatoNeRF, that allows for creating vivid, accurate 3D models of a scene from a single camera view. The innovative technology uses the shadowing in a scene to determine what could lie within obstructed areas. By combining machine learning with LIDAR (Light Detection and…

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A novel approach to computer vision accelerates the screening process of electronic components.

3-D printing, Algorithms, Artificial Intelligence, Automation, Computer vision, Imaging, Machine learning, Materials science and engineering, Mechanical engineering, Renewable energy, Research, School of Engineering, Solar, Sustainability, UncategorizedJune 12, 202433Views0Likes0Comments
Solar cells, transistors, LEDs, and batteries with boosted performance require better electronic materials which are often discovered from novel compositions. Scientists have turned to AI tools to identify potential materials from millions of chemical formulations, with engineers developing machines that can print hundreds of samples at a time, based on compositions identified by AI algorithms.…

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The diffusion control model can alter the characteristics of the material present in pictures.

Artificial Intelligence, Computer graphics, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Computer vision, Electrical Engineering & Computer Science (eecs), Games, Imaging, Machine learning, MIT Schwarzman College of Computing, Research, School of Engineering, UncategorizedMay 29, 202434Views0Likes0Comments
A team of researchers from the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) and Google Research have developed an image-to-image diffusion model called Alchemist, which allows users to modify the material properties of objects in photos. The system adjusts aspects such as roughness, metallicity, innate color (albedo), and transparency, and can be applied to…

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Charting the neural routes associated with visual recall in the brain.

Artificial Intelligence, Brain and cognitive sciences, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Electrical Engineering & Computer Science (eecs), Functional magnetic resonance imaging (fMRI), Image Processing, Imaging, Machine learning, MIT Schwarzman College of Computing, MIT-IBM Watson AI Lab, Neuroscience, Research, School of Engineering, Uncategorized, VisionApril 24, 202438Views0Likes0Comments
For almost ten years, researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) have conducted studies to understand why some images are more memorable than others. The team used magnetoencephalography (MEG), which records timing of brain activity, and functional magnetic resonance imaging (fMRI), which identifies active brain regions, to discern when and where in…

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A novel approach to AI successfully encapsulates ambiguity present in medical imagery.

Artificial Intelligence, Broad Institute, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Electrical Engineering & Computer Science (eecs), Health care, Imaging, Machine learning, MIT Schwarzman College of Computing, National Institutes of Health (NIH), Research, School of Engineering, UncategorizedApril 12, 202437Views0Likes0Comments
In the field of biomedicine, segmentation refers to the process of highlighting important structures in a medical image, from organs to cells. Artificial intelligence (AI) models are starting to play a pivotal role in this task, but there are limitations with most existing models, mainly due to the fact that they are unable to factor…

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A novel Artificial Intelligence technique records ambiguity within medical imaging.

Artificial Intelligence, Broad Institute, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Electrical Engineering & Computer Science (eecs), Health care, Imaging, Machine learning, MIT Schwarzman College of Computing, National Institutes of Health (NIH), Research, School of Engineering, UncategorizedApril 12, 202438Views0Likes0Comments
A team at MIT, along with the Broad Institute of MIT and Harvard, and Massachusetts General Hospital, has developed an artificial intelligence (AI) tool that can help navigate the uncertainty in medical image analysis. The tool, named Tyche, provides multiple possible interpretations of a medical image rather than the single answer typically provided by AI…

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New algorithm delivers detailed understanding for computer vision.

Algorithms, Artificial Intelligence, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Computer vision, Electrical Engineering & Computer Science (eecs), Imaging, Machine learning, MIT Schwarzman College of Computing, National Science Foundation (NSF), Research, School of Engineering, UncategorizedMarch 19, 202437Views0Likes0Comments
MIT researchers have developed an algorithm called FeatUp that enables computer vision algorithms to capture both high-level details and fine-grained minutiae of a scene simultaneously. Modern computer vision algorithms, like human beings, can only recall the broad details of a scene while the more nuanced specifics are often lost. To understand an image, they break…

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