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Faculty and students are exploring a number of critical problems in the area of computer vision, with a focus on the analysis and modeling of visual scenes from static images as well as video sequences. Research goals include: i) the semantic understanding of materials, objects, and actions within a scene; ii) modeling the spatial organization and layout of the scene and its behavior in time. The algorithms developed in this area of research enable the design of machines that can perform real-world visual tasks such as autonomous navigation, visual surveillance, or content-based image and video indexing.
Live public street cams are tracking social distancing
Voxel51, a U-M startup led by Prof. Jason Corso, uses custom AI to continuously track vehicle, cyclist, and pedestrian traffic in real time at some of the most visited places in the world.
Creating a place where kids of all abilities can play together
Prof. Hun-Seok Kim helped design iGYM, an augmented reality system that allows disabled and able-bodied people to play physical games together.
Computer vision: Finding the best teaching frame in a video for fake video fightback
The frame in which a human marks out the boundaries of an object makes a huge difference in how well AI software can identify that object through the rest of the video.
Prof. Jason Corso on artificial intelligence
The most exciting use of AI for me focuses around a better collective use of our available resources, says Corso.
Paper award for training computer vision systems more accurately
PhD student Jean Young Song offers an improved solution to the problem of image segmentation.
Kyle Min awarded Towner Prize for Distinguished Academic Achievement
Kyle Min researches how computer vision can analyze law enforcement body cameras.
$1.6M toward artificial intelligence for data science
DARPA is trying to build a system that can turn large data sets into models that can make predictions, and U-M is in on the project.
COVE: a tool for advancing progress in computer vision
Centralizing available data in the intelligent systems community through a COmputer Vision Exchange for Data, Annotations and Tools, called COVE.
Bourne Pursuit: Improving computer tracking of human activity
Researchers have found a way to improve a computer’s human-tracking accuracy by looking at where the targets are going, but also at what they’re doing.
Silvio Savarese’s research applying computer vision techniques to construction sites leads to best paper award and a new spinoff company
“We have pioneered an integrated scene understanding framework that enables the automatic tracking of structural changes, allowing data to be collected easily.”
Computer Vision Course is part of a groundbreaking online initiative
Computer Vision seeks to imitate humans’ ability to recognize objects, navigate scenes, reconstruct layouts, and understand the geometric space and semantic meaning.
Srinath Sridhar awarded Rackham International Student Fellowship
Srinath’s research focuses on using computer vision techniques such as markerless camera tracking for creating augmented reality AR environments.
Sid Bao earns Best Student Paper Award for Computer Vision Research
Bao’s research is in Semantic Structure from Motion, a new framework for jointly recognizing objects as well as reconstructing their underlying 3D geometry.
Silvio Savarese authors book in the field of Computer Vision
“This book organizes and introduces major concepts in 3D scene and object representation and inference from still images.”
Computer Vision Research Recognized at Innovation in AEC Conference
With D4AR models, you can monitor progress, productivity, safety, quality, constructability and even site logistics remotely.